{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batch Normalization\n",
    "One way to make deep networks easier to train is to use more sophisticated optimization procedures such as SGD+momentum, RMSProp, or Adam. Another strategy is to change the architecture of the network to make it easier to train. \n",
    "One idea along these lines is batch normalization which was proposed by [3] in 2015.\n",
    "\n",
    "The idea is relatively straightforward. Machine learning methods tend to work better when their input data consists of uncorrelated features with zero mean and unit variance. When training a neural network, we can preprocess the data before feeding it to the network to explicitly decorrelate its features; this will ensure that the first layer of the network sees data that follows a nice distribution. However, even if we preprocess the input data, the activations at deeper layers of the network will likely no longer be decorrelated and will no longer have zero mean or unit variance since they are output from earlier layers in the network. Even worse, during the training process the distribution of features at each layer of the network will shift as the weights of each layer are updated.\n",
    "\n",
    "The authors of [3] hypothesize that the shifting distribution of features inside deep neural networks may make training deep networks more difficult. To overcome this problem, [3] proposes to insert batch normalization layers into the network. At training time, a batch normalization layer uses a minibatch of data to estimate the mean and standard deviation of each feature. These estimated means and standard deviations are then used to center and normalize the features of the minibatch. A running average of these means and standard deviations is kept during training, and at test time these running averages are used to center and normalize features.\n",
    "\n",
    "It is possible that this normalization strategy could reduce the representational power of the network, since it may sometimes be optimal for certain layers to have features that are not zero-mean or unit variance. To this end, the batch normalization layer includes learnable shift and scale parameters for each feature dimension.\n",
    "\n",
    "[3] [Sergey Ioffe and Christian Szegedy, \"Batch Normalization: Accelerating Deep Network Training by Reducing\n",
    "Internal Covariate Shift\", ICML 2015.](https://arxiv.org/abs/1502.03167)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# As usual, a bit of setup\n",
    "import time\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from cs231n.classifiers.fc_net import *\n",
    "from cs231n.data_utils import get_CIFAR10_data\n",
    "from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n",
    "from cs231n.solver import Solver\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "    \"\"\" returns relative error \"\"\"\n",
    "    return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))\n",
    "\n",
    "def print_mean_std(x,axis=0):\n",
    "    print('  means: ', x.mean(axis=axis))\n",
    "    print('  stds:  ', x.std(axis=axis))\n",
    "    print('') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('X_val: ', (1000, 3, 32, 32))\n",
      "('X_train: ', (49000, 3, 32, 32))\n",
      "('X_test: ', (1000, 3, 32, 32))\n",
      "('y_val: ', (1000,))\n",
      "('y_train: ', (49000,))\n",
      "('y_test: ', (1000,))\n"
     ]
    }
   ],
   "source": [
    "# Load the (preprocessed) CIFAR10 data.\n",
    "data = get_CIFAR10_data()\n",
    "for k, v in data.items():\n",
    "  print('%s: ' % k, v.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batch normalization: forward\n",
    "In the file `cs231n/layers.py`, implement the batch normalization forward pass in the function `batchnorm_forward`. Once you have done so, run the following to test your implementation.\n",
    "\n",
    "Referencing the paper linked to above would be helpful!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before batch normalization:\n",
      "('  means: ', array([ -2.3814598 , -13.18038246,   1.91780462]))\n",
      "('  stds:  ', array([27.18502186, 34.21455511, 37.68611762]))\n",
      "\n",
      "After batch normalization (gamma=1, beta=0)\n",
      "('  means: ', array([ 1.33226763e-17, -3.94129174e-17,  3.29597460e-17]))\n",
      "('  stds:  ', array([0.99999999, 1.        , 1.        ]))\n",
      "\n",
      "('After batch normalization (gamma=', array([1., 2., 3.]), ', beta=', array([11., 12., 13.]), ')')\n",
      "('  means: ', array([11., 12., 13.]))\n",
      "('  stds:  ', array([0.99999999, 1.99999999, 2.99999999]))\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Check the training-time forward pass by checking means and variances\n",
    "# of features both before and after batch normalization   \n",
    "\n",
    "# Simulate the forward pass for a two-layer network\n",
    "np.random.seed(231)\n",
    "N, D1, D2, D3 = 200, 50, 60, 3\n",
    "X = np.random.randn(N, D1)\n",
    "W1 = np.random.randn(D1, D2)\n",
    "W2 = np.random.randn(D2, D3)\n",
    "a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "\n",
    "print('Before batch normalization:')\n",
    "print_mean_std(a,axis=0)\n",
    "\n",
    "gamma = np.ones((D3,))\n",
    "beta = np.zeros((D3,))\n",
    "# Means should be close to zero and stds close to one\n",
    "print('After batch normalization (gamma=1, beta=0)')\n",
    "a_norm, _ = batchnorm_forward(a, gamma, beta, {'mode': 'train'})\n",
    "print_mean_std(a_norm,axis=0)\n",
    "\n",
    "gamma = np.asarray([1.0, 2.0, 3.0])\n",
    "beta = np.asarray([11.0, 12.0, 13.0])\n",
    "# Now means should be close to beta and stds close to gamma\n",
    "print('After batch normalization (gamma=', gamma, ', beta=', beta, ')')\n",
    "a_norm, _ = batchnorm_forward(a, gamma, beta, {'mode': 'train'})\n",
    "print_mean_std(a_norm,axis=0)"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 3,
=======
   "execution_count": 4,
>>>>>>> e8168844d4356e446b3e0b9766c85e624d86e7f9
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After batch normalization (test-time):\n",
      "('  means: ', array([-0.03927354, -0.04349152, -0.10452688]))\n",
      "('  stds:  ', array([1.01531428, 1.01238373, 0.97819988]))\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Check the test-time forward pass by running the training-time\n",
    "# forward pass many times to warm up the running averages, and then\n",
    "# checking the means and variances of activations after a test-time\n",
    "# forward pass.\n",
    "\n",
    "np.random.seed(231)\n",
    "N, D1, D2, D3 = 200, 50, 60, 3\n",
    "W1 = np.random.randn(D1, D2)\n",
    "W2 = np.random.randn(D2, D3)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "gamma = np.ones(D3)\n",
    "beta = np.zeros(D3)\n",
    "\n",
    "for t in range(50):\n",
    "  X = np.random.randn(N, D1)\n",
    "  a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "  batchnorm_forward(a, gamma, beta, bn_param)\n",
    "\n",
    "bn_param['mode'] = 'test'\n",
    "X = np.random.randn(N, D1)\n",
    "a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "a_norm, _ = batchnorm_forward(a, gamma, beta, bn_param)\n",
    "\n",
    "# Means should be close to zero and stds close to one, but will be\n",
    "# noisier than training-time forward passes.\n",
    "print('After batch normalization (test-time):')\n",
    "print_mean_std(a_norm,axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batch normalization: backward\n",
    "Now implement the backward pass for batch normalization in the function `batchnorm_backward`.\n",
    "\n",
    "To derive the backward pass you should write out the computation graph for batch normalization and backprop through each of the intermediate nodes. Some intermediates may have multiple outgoing branches; make sure to sum gradients across these branches in the backward pass.\n",
    "\n",
    "Once you have finished, run the following to numerically check your backward pass."
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 4,
=======
   "execution_count": 5,
>>>>>>> e8168844d4356e446b3e0b9766c85e624d86e7f9
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('dx error: ', 1.7029275364845641e-09)\n",
      "('dgamma error: ', 7.420414216247087e-13)\n",
      "('dbeta error: ', 2.8795057655839487e-12)\n"
     ]
    }
   ],
   "source": [
    "# Gradient check batchnorm backward pass\n",
    "np.random.seed(231)\n",
    "N, D = 4, 5\n",
    "x = 5 * np.random.randn(N, D) + 12\n",
    "gamma = np.random.randn(D)\n",
    "beta = np.random.randn(D)\n",
    "dout = np.random.randn(N, D)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "fx = lambda x: batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "fg = lambda a: batchnorm_forward(x, a, beta, bn_param)[0]\n",
    "fb = lambda b: batchnorm_forward(x, gamma, b, bn_param)[0]\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(fx, x, dout)\n",
    "da_num = eval_numerical_gradient_array(fg, gamma.copy(), dout)\n",
    "db_num = eval_numerical_gradient_array(fb, beta.copy(), dout)\n",
    "\n",
    "_, cache = batchnorm_forward(x, gamma, beta, bn_param)\n",
    "dx, dgamma, dbeta = batchnorm_backward(dout, cache)\n",
    "#You should expect to see relative errors between 1e-13 and 1e-8\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dgamma error: ', rel_error(da_num, dgamma))\n",
    "print('dbeta error: ', rel_error(db_num, dbeta))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batch normalization: alternative backward\n",
    "In class we talked about two different implementations for the sigmoid backward pass. One strategy is to write out a computation graph composed of simple operations and backprop through all intermediate values. Another strategy is to work out the derivatives on paper. For example, you can derive a very simple formula for the sigmoid function's backward pass by simplifying gradients on paper.\n",
    "\n",
    "Surprisingly, it turns out that you can do a similar simplification for the batch normalization backward pass too.  \n",
    "Given a set of inputs $X=\\begin{bmatrix}x_1\\\\x_2\\\\...\\\\x_N\\end{bmatrix}$, \n",
    "we first calculate the mean $\\mu=\\frac{1}{N}\\sum_{k=1}^N x_k$ and variance $v=\\frac{1}{N}\\sum_{k=1}^N (x_k-\\mu)^2.$    \n",
    "With $\\mu$ and $v$ calculated, we can calculate the standard deviation $\\sigma=\\sqrt{v+\\epsilon}$  and normalized data $Y$ with $y_i=\\frac{x_i-\\mu}{\\sigma}.$\n",
    "\n",
    "\n",
    "The meat of our problem is to get $\\frac{\\partial L}{\\partial X}$ from the upstream gradient $\\frac{\\partial L}{\\partial Y}.$ It might be challenging to directly reason about the gradients over $X$ and $Y$ - try reasoning about it in terms of $x_i$ and $y_i$ first.\n",
    "\n",
    "You will need to come up with the derivations for $\\frac{\\partial L}{\\partial x_i}$, by relying on the Chain Rule to first calculate the intermediate $\\frac{\\partial \\mu}{\\partial x_i}, \\frac{\\partial v}{\\partial x_i}, \\frac{\\partial \\sigma}{\\partial x_i},$ then assemble these pieces to calculate $\\frac{\\partial y_i}{\\partial x_i}$. You should make sure each of the intermediary steps are all as simple as possible. \n",
    "\n",
    "After doing so, implement the simplified batch normalization backward pass in the function `batchnorm_backward_alt` and compare the two implementations by running the following. Your two implementations should compute nearly identical results, but the alternative implementation should be a bit faster."
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 5,
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   "execution_count": 6,
>>>>>>> e8168844d4356e446b3e0b9766c85e624d86e7f9
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('dx difference: ', 3.0666812374106965e-13)\n",
      "('dgamma difference: ', 0.0)\n",
      "('dbeta difference: ', 0.0)\n",
<<<<<<< HEAD
      "speedup: 1.96x\n"
=======
      "speedup: 1.97x\n"
>>>>>>> e8168844d4356e446b3e0b9766c85e624d86e7f9
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D = 100, 500\n",
    "x = 5 * np.random.randn(N, D) + 12\n",
    "gamma = np.random.randn(D)\n",
    "beta = np.random.randn(D)\n",
    "dout = np.random.randn(N, D)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "out, cache = batchnorm_forward(x, gamma, beta, bn_param)\n",
    "\n",
    "t1 = time.time()\n",
    "dx1, dgamma1, dbeta1 = batchnorm_backward(dout, cache)\n",
    "t2 = time.time()\n",
    "dx2, dgamma2, dbeta2 = batchnorm_backward_alt(dout, cache)\n",
    "t3 = time.time()\n",
    "\n",
    "print('dx difference: ', rel_error(dx1, dx2))\n",
    "print('dgamma difference: ', rel_error(dgamma1, dgamma2))\n",
    "print('dbeta difference: ', rel_error(dbeta1, dbeta2))\n",
    "print('speedup: %.2fx' % ((t2 - t1) / (t3 - t2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fully Connected Nets with Batch Normalization\n",
    "Now that you have a working implementation for batch normalization, go back to your `FullyConnectedNet` in the file `cs231n/classifiers/fc_net.py`. Modify your implementation to add batch normalization.\n",
    "\n",
    "Concretely, when the `normalization` flag is set to `\"batchnorm\"` in the constructor, you should insert a batch normalization layer before each ReLU nonlinearity. The outputs from the last layer of the network should not be normalized. Once you are done, run the following to gradient-check your implementation.\n",
    "\n",
    "HINT: You might find it useful to define an additional helper layer similar to those in the file `cs231n/layer_utils.py`. If you decide to do so, do it in the file `cs231n/classifiers/fc_net.py`."
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 6,
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   "execution_count": 7,
>>>>>>> e8168844d4356e446b3e0b9766c85e624d86e7f9
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Running check with reg = ', 0)\n",
      "('Initial loss: ', 2.2611955101340957)\n",
      "W1 relative error: 1.10e-04\n",
      "W2 relative error: 2.85e-06\n",
      "W3 relative error: 3.92e-10\n",
      "b1 relative error: 2.22e-03\n",
      "b2 relative error: 2.44e-07\n",
      "b3 relative error: 4.78e-11\n",
      "beta1 relative error: 7.33e-09\n",
      "beta2 relative error: 1.89e-09\n",
<<<<<<< HEAD
      "gamma1 relative error: 7.47e-09\n",
      "gamma2 relative error: 2.41e-09\n",
      "()\n",
=======
      "gamma1 relative error: 6.96e-09\n",
      "gamma2 relative error: 1.96e-09\n",
      "\n",
>>>>>>> e8168844d4356e446b3e0b9766c85e624d86e7f9
      "('Running check with reg = ', 3.14)\n",
      "('Initial loss: ', 6.996533220108303)\n",
      "W1 relative error: 1.98e-06\n",
      "W2 relative error: 2.28e-06\n",
      "W3 relative error: 1.11e-08\n",
      "b1 relative error: 1.11e-08\n",
      "b2 relative error: 2.78e-08\n",
      "b3 relative error: 2.23e-10\n",
      "beta1 relative error: 6.32e-09\n",
      "beta2 relative error: 5.69e-09\n",
      "gamma1 relative error: 5.94e-09\n",
      "gamma2 relative error: 4.14e-09\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D, H1, H2, C = 2, 15, 20, 30, 10\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=(N,))\n",
    "\n",
    "# You should expect losses between 1e-4~1e-10 for W, \n",
    "# losses between 1e-08~1e-10 for b,\n",
    "# and losses between 1e-08~1e-09 for beta and gammas.\n",
    "for reg in [0, 3.14]:\n",
    "  print('Running check with reg = ', reg)\n",
    "  model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n",
    "                            reg=reg, weight_scale=5e-2, dtype=np.float64,\n",
    "                            normalization='batchnorm')\n",
    "\n",
    "  loss, grads = model.loss(X, y)\n",
    "  print('Initial loss: ', loss)\n",
    "    \n",
    "  # print(grads['gamma2'])\n",
    "\n",
    "  for name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n",
    "    # print(name, grad_num.shape, 'my grads', grads[name].shape)\n",
    "    print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))\n",
    "  if reg == 0: print('')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batchnorm for deep networks\n",
    "Run the following to train a six-layer network on a subset of 1000 training examples both with and without batch normalization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 200) loss: 2.340975\n",
      "(Epoch 0 / 10) train acc: 0.107000; val_acc: 0.115000\n",
      "(Epoch 1 / 10) train acc: 0.333000; val_acc: 0.265000\n",
      "(Iteration 21 / 200) loss: 1.999443\n",
      "(Epoch 2 / 10) train acc: 0.425000; val_acc: 0.298000\n",
      "(Iteration 41 / 200) loss: 2.052620\n",
      "(Epoch 3 / 10) train acc: 0.453000; val_acc: 0.294000\n",
      "(Iteration 61 / 200) loss: 1.810844\n",
      "(Epoch 4 / 10) train acc: 0.519000; val_acc: 0.305000\n",
      "(Iteration 81 / 200) loss: 1.331293\n",
      "(Epoch 5 / 10) train acc: 0.578000; val_acc: 0.302000\n",
      "(Iteration 101 / 200) loss: 1.385208\n",
      "(Epoch 6 / 10) train acc: 0.607000; val_acc: 0.329000\n",
      "(Iteration 121 / 200) loss: 1.231006\n",
      "(Epoch 7 / 10) train acc: 0.666000; val_acc: 0.311000\n",
      "(Iteration 141 / 200) loss: 1.193788\n",
      "(Epoch 8 / 10) train acc: 0.685000; val_acc: 0.339000\n",
      "(Iteration 161 / 200) loss: 0.769957\n",
      "(Epoch 9 / 10) train acc: 0.744000; val_acc: 0.336000\n",
      "(Iteration 181 / 200) loss: 1.096398\n",
      "(Epoch 10 / 10) train acc: 0.784000; val_acc: 0.310000\n",
      "(Iteration 1 / 200) loss: 2.302332\n",
      "(Epoch 0 / 10) train acc: 0.126000; val_acc: 0.130000\n",
      "(Epoch 1 / 10) train acc: 0.235000; val_acc: 0.199000\n",
      "(Iteration 21 / 200) loss: 2.115901\n",
      "(Epoch 2 / 10) train acc: 0.296000; val_acc: 0.249000\n",
      "(Iteration 41 / 200) loss: 1.897200\n",
      "(Epoch 3 / 10) train acc: 0.351000; val_acc: 0.291000\n",
      "(Iteration 61 / 200) loss: 1.730721\n",
      "(Epoch 4 / 10) train acc: 0.389000; val_acc: 0.298000\n",
      "(Iteration 81 / 200) loss: 1.531783\n",
      "(Epoch 5 / 10) train acc: 0.426000; val_acc: 0.274000\n",
      "(Iteration 101 / 200) loss: 1.532540\n",
      "(Epoch 6 / 10) train acc: 0.492000; val_acc: 0.319000\n",
      "(Iteration 121 / 200) loss: 1.335824\n",
      "(Epoch 7 / 10) train acc: 0.534000; val_acc: 0.330000\n",
      "(Iteration 141 / 200) loss: 1.322314\n",
      "(Epoch 8 / 10) train acc: 0.583000; val_acc: 0.333000\n",
      "(Iteration 161 / 200) loss: 1.244997\n",
      "(Epoch 9 / 10) train acc: 0.605000; val_acc: 0.317000\n",
      "(Iteration 181 / 200) loss: 1.124863\n",
      "(Epoch 10 / 10) train acc: 0.686000; val_acc: 0.331000\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "# Try training a very deep net with batchnorm\n",
    "hidden_dims = [100, 100, 100, 100, 100]\n",
    "\n",
    "num_train = 1000\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "weight_scale = 2e-2\n",
    "bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization='batchnorm')\n",
    "model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=None)\n",
    "\n",
    "bn_solver = Solver(bn_model, small_data,\n",
    "                num_epochs=10, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                verbose=True,print_every=20)\n",
    "bn_solver.train()\n",
    "\n",
    "solver = Solver(model, small_data,\n",
    "                num_epochs=10, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                verbose=True, print_every=20)\n",
    "solver.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the following to visualize the results from two networks trained above. You should find that using batch normalization helps the network to converge much faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zSOjqRZzKlL2pYkkFTAAAAKBmkNDVi2yVKWXBGrfMwiW9qWJJBUwAAACgZpDQ1YtulSkl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\nT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5uBD3I3Au8ayoM7597dddnMbgaWKczJiHMOqtb6ELfpIWhvgvyFcNWdvhuX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\nERERkSClgk5ERERERCRI/X/4kCy9J1YTUQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faf28704790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_training_history(title, label, baseline, bn_solvers, plot_fn, bl_marker='.', bn_marker='.', labels=None):\n",
    "    \"\"\"utility function for plotting training history\"\"\"\n",
    "    plt.title(title)\n",
    "    plt.xlabel(label)\n",
    "    bn_plots = [plot_fn(bn_solver) for bn_solver in bn_solvers]\n",
    "    bl_plot = plot_fn(baseline)\n",
    "    num_bn = len(bn_plots)\n",
    "    for i in range(num_bn):\n",
    "        label='with_norm'\n",
    "        if labels is not None:\n",
    "            label += str(labels[i])\n",
    "        plt.plot(bn_plots[i], bn_marker, label=label)\n",
    "    label='baseline'\n",
    "    if labels is not None:\n",
    "        label += str(labels[0])\n",
    "    plt.plot(bl_plot, bl_marker, label=label)\n",
    "    plt.legend(loc='lower center', ncol=num_bn+1) \n",
    "\n",
    "    \n",
    "plt.subplot(3, 1, 1)\n",
    "plot_training_history('Training loss','Iteration', solver, [bn_solver], \\\n",
    "                      lambda x: x.loss_history, bl_marker='o', bn_marker='o')\n",
    "plt.subplot(3, 1, 2)\n",
    "plot_training_history('Training accuracy','Epoch', solver, [bn_solver], \\\n",
    "                      lambda x: x.train_acc_history, bl_marker='-o', bn_marker='-o')\n",
    "plt.subplot(3, 1, 3)\n",
    "plot_training_history('Validation accuracy','Epoch', solver, [bn_solver], \\\n",
    "                      lambda x: x.val_acc_history, bl_marker='-o', bn_marker='-o')\n",
    "\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batch normalization and initialization\n",
    "We will now run a small experiment to study the interaction of batch normalization and weight initialization.\n",
    "\n",
    "The first cell will train 8-layer networks both with and without batch normalization using different scales for weight initialization. The second layer will plot training accuracy, validation set accuracy, and training loss as a function of the weight initialization scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running weight scale 1 / 20\n",
      "Running weight scale 2 / 20\n",
      "Running weight scale 3 / 20\n",
      "Running weight scale 4 / 20\n",
      "Running weight scale 5 / 20\n",
      "Running weight scale 6 / 20\n",
      "Running weight scale 7 / 20\n",
      "Running weight scale 8 / 20\n",
      "Running weight scale 9 / 20\n",
      "Running weight scale 10 / 20\n",
      "Running weight scale 11 / 20\n",
      "Running weight scale 12 / 20\n",
      "Running weight scale 13 / 20\n",
      "Running weight scale 14 / 20\n",
      "Running weight scale 15 / 20\n",
      "Running weight scale 16 / 20\n",
      "Running weight scale 17 / 20\n",
      "Running weight scale 18 / 20\n",
      "Running weight scale 19 / 20\n",
      "Running weight scale 20 / 20\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "# Try training a very deep net with batchnorm\n",
    "hidden_dims = [50, 50, 50, 50, 50, 50, 50]\n",
    "num_train = 1000\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "bn_solvers_ws = {}\n",
    "solvers_ws = {}\n",
    "weight_scales = np.logspace(-4, 0, num=20)\n",
    "for i, weight_scale in enumerate(weight_scales):\n",
    "  print('Running weight scale %d / %d' % (i + 1, len(weight_scales)))\n",
    "  bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization='batchnorm')\n",
    "  model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=None)\n",
    "\n",
    "  bn_solver = Solver(bn_model, small_data,\n",
    "                  num_epochs=10, batch_size=50,\n",
    "                  update_rule='adam',\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-3,\n",
    "                  },\n",
    "                  verbose=False, print_every=200)\n",
    "  bn_solver.train()\n",
    "  bn_solvers_ws[weight_scale] = bn_solver\n",
    "\n",
    "  solver = Solver(model, small_data,\n",
    "                  num_epochs=10, batch_size=50,\n",
    "                  update_rule='adam',\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-3,\n",
    "                  },\n",
    "                  verbose=False, print_every=200)\n",
    "  solver.train()\n",
    "  solvers_ws[weight_scale] = solver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yteMrAQPDfQ4iUqxUGioiImVLxm4YOdyPXpz1HHQbHnZEEqXGz1vHtSNm8vtj\nm3PPkE5hh1MysjJh5bd+SYpF42DHujw3S01oRNXbFpVycCJSVEUpDS2hFVBFREQOQuYeGHUJLJ8I\ng59SEiihWZ68k1tHz6FbsyT+PrBD2OGUnNg4aNEbBj4CNy8A8m6CUzVtPWRnl25sIlKilAiKiEjZ\nkJUBoy+DpRPgzMeh+yVhRyRRateeTK5540cqx8fy3IXdqRQXJW+XYmIgsWk+dzp4ojNMvBc2LSvV\nsESkZETJXzYRESnTsjLg3cth8ccw4BHocXnYEUmUcs5xx/vzWJa8k6eGHRl9C6z3vRPi933Oe6wy\n9LoS6reHKY/DM0fBy6fBjFchLSWkQEXkUCkRFBGRcGVlwvtX+sYV/f7l33CKhOSN6b8yZs5abjmt\nLSe0qRt2OKWvy1AY9JRfkB4jJb4Bd2Reyc6+D8JF7/ry0dPuhT3bYdxN8Ehb/yHO0i8gOyvs6EWk\nCNQsRkREwpOdBR9cDfNG+zeXx98YdkQSxWau3Mr5L0zjxDb1ePmSHsTElNFF40vR9z9vYegL03hy\nWDeGdMu1oLxzsHYWzH7Ldx1N2wo1GvlEsutwqH9EeEGLRDE1ixERkbIvOxs+us4ngX3vVBIoodq8\ncw9/GjGTBjWr8PjQbkoCAz2a16J+jcqMn7dfN1EzaNLdN5m5ZTEMfR0adYNvn4HnjoYX+8D3L0Hq\nlnACF5FCKREUEZHSl50NY2+AOW/ByX+DE28JOyKJYlnZjhtHzmbzrnT+c9FRJFYtR4vGl7CYGOOM\nTg35enEyu/Zk5r1RXGXoMASGj4RbFkG/ByArHcb/BR5tB+9cDIs/9XOBRaTMUCIocrDmjoLHO8Hd\nSf7r3FFhRyTRpDyff87B+Ftg1hvQ+1Y4+bawI5Io98QXS5iybBP3DelIpyaJYYdT5gzs0pg9mdlM\nXLSx8I2r14dj/wR/nApXfwM9/wC/fgtvnw+PtYcJf4f180s+aBEplBJBkYMxd5Qfzdi2CnD+69gb\nytebcSm/yvP55xx88leY8QocfxP0+XvYEUmU+3LRBp7+chlDezTl/J6HhR1OmfRbeejcvBebz1ej\nLtD/X36UcNjb0Oxo+O4F+M/x8J8TYfrzsGtTyQQtIoVSIihyMCbeCxlp+96WkeZvFylJmXvgs//L\n+/z74u5QQoqYczDhb/D9i3DsdXDq3X6ekUhIVm1J5aaRs+nQqCb3DukUdjhlVk556FeLN+ZfHlqQ\n2Hg4YgD5xAgfAAAgAElEQVQMG+HnE57xEFgMfHq7Lx19ezgsHAuZ6cUfvIjkKy7sAETKpW2ri3a7\nSFFkZ8P21bB5GWxeHnxdBpuW+tE/l53347avgX8fDkmH+UviYXu/z7lUqVmqT+U3zsHnd8L05+Do\na+D0fyoJlFDtzsjimjd/BOA/Fx1FlfjYkCMq2wZ0bsRr035l4qKNDO7a+OB3VK0OHH21v2xY4OcJ\nzx3l1xBNqA2dz4Nuw6FRV/2NEClhSgRFDkbV2pC6+cDbE5uWfixSfqVu2Zvg5SR7m5fDluWQuXvv\ndpWqQ51W0LQHdB3mO/Gl5dGJr0oidDwHUlZC8hJYNhEyUvfbJgmSmkFS830TxMRm/mtCUvE/T+fg\ny/vg26egxxXQ/0G9wZPQ3T3mJ35au52XL+nBYXWqhh1Omdfj8NrUC8pDDykRzK1BB/+hUN+7YfmX\nPin88VX4/gWo38EnhJ2HQo0GxXM8EdmHEkGRolo/H3Zv92UtuUdmYuJ8C3yR3NJTYcuKXIlerkva\n1r3bxcRBrcOhTmto1cd/zbnUaLhv4lSntZ8TmLs8ND4BBjzi1/DK4Zz/wCLlV58cpqyElFX+6+bl\nsPwryNi1b7yVE3MliM3yGFFMiiyJmzvKl0pvWw2Va/jFp7v/3seoJFBCNuqHVYz8YRV/6tOKUzso\nyYhEbFAe+s4Pq9i1J5NqlYvxLWRsHLQ93V/StsL89/36hJ/9Az6/C1qfCt0ugLZnQHyVff++JDb1\n/3tz/+0TkYgoERQpitQtMHI4VKvr291PfdL/I6pUDdJ3wp4dYUcoxaUobzSys3yylbuMc/My2LTM\nl3jmVqOxH93rcBbUbbM32Us6zM+jiUROHIXFZ+bP1Wp1oclRB+7HOX9Op/zqS05/SxZXwtaf4edJ\n/rzOrVKNA5PD3xLG5pBQy68LmDtR3bMdLBaaHw8xmpou4Zq/Zhv/99F8jm9dhz+f1i7scMqVAZ0b\n8fq0X/ly0UYGFdeo4P4SakHPK/wleQnMeRvmjITRE/wHUY26wsrpkLXHb5/TLAuUDIoUkTnnwo6h\nWPTo0cPNmDEj7DCkIsvKhBHn+jbYl30KTXO9sc7OgrcvgGVfwIWj/KeXUn7ldOXcf8TttH/6Uqbf\nSjmDxG/rz37NrByVE6FurhG9Oq2gThuo3RIqVy/953OwnPOfzudOEFNW7k0at/4K6ft9+FGpui9r\nzc6joURiM7hZbeMlPNtSMxj0zBTSM7MZd8MJ1K1eOeyQypWsbMfRD0yk5+G1eP6iPD5cKinZWf6D\nqdlv+Q+a8qK/LyIAmNmPzrkekWyrEUGRSH1xF6z4GoY8u28SCBATC7/7L7xyBoy6FK6YAA06hhGl\nHKysTNi1EXasg0/vyLsr5/hci57HVoLarfyoXrsz9i3lrFa3YpQ/mvn5sFVrQ+NuB97vHOxO2bfk\nNGUlfPd83vtTMyUJUXa245bRs1m3LY13rj5WSeBByCkPHf3jKlLTM6laqZTeRsbEQqtT/GXeu0Ae\ngxj6+yJSZEoERSIxdxRMewZ6XQ1HXpT3NpVrwPB34OW+8Nb58Icv/NwuCVdWBuzcADs2+CRv5/pc\n3wdfd2yAXcnk+eZifxe955O9xGb+zUk0M/NlXAm1fLlWjkXjgjUO96NmShKi5yct54uFG7lncEe6\nH1Yr7HDKrQGdG/HGdF8eemaXEioPLUhi07z/vlgMzHwDul7g5xyKSKH0ShEpzNrZMOZ6aH4C9Lu/\n4G0Tm8AFI+HVM+DtYXDpx37+oBzoUCf7Z6b7RO63ZG69v+wMvuYke6mbOSDBsxioVt93oqvZBBp3\nhxqN/PXqDWHcTX6/+0tsprLfSPS9M+/SWjVTkpBMXbaJRz9bzOCujbnk2OZhh1Ou9WpRm7rVKzN+\n3rpwEsG8/r7EVvYfvI65DqY+AX3+7udha06ySIGUCIoUZGcyjLwQqtWDoa9F1syjcTc497++qcz7\nV8HQN/TPaH/7z8HLPdm/w5AguVufR3KX63pey3dYLFSv798QJDb1yy3UaOgv1Rv6RK9GI//7LGg0\nL32nEplDEWkzG5FSsG5bGje8PYtW9arzr3M6YxWhbDtEoZWH5sjv70vn82DRx/DlP+Hdy6DBY9D3\n/6DN6RWjVF+kBKhZjEh+sjLg9bNgzQy4fELec6QKMu05mHAHHHe9XydJ9nq8U/6lPXktlh4TB9Ub\n+EvOyF2NRvter97Qz80rrnJNtScXKbc+nLWGhycsZm1KGnGxhgHjb+xN6/rlqFlTGTZt+WYueGk6\nzw7vzsAujcIOZ1/ZWTD/Pfjqftj6CzTt5f9+tzgx7MhESkWxNosxs1jnXNahhyVSzkz4O/w6Bc55\nqehJIMAxf/QLg3/7tG8q0uOy4o+xvMpvUr/Lhj7/2DuKlzOSV7VO6Y+qdhmqxE+kHPpw1hrueH8e\naRn+rUtGliM+1pi/ZpsSwWKSUx768by1ZS8RjIn1f7s7ng2z3oBJD8NrZ0LLPn6EMK+ldESiVCTv\nrJaa2cNm1qHEoxEpK2a9Cd+/AMded/DJgBn0/ze0Pg0+vgWWTSzeGMurPTt8x828JDaDk26F7hdD\nm9OgYWeoXk+ltSISsYcnLP4tCcyRkeV4eMLikCKqeGJjjP6dGvDloo2kpuexVExZEBsPPS6HG2bC\n6ffD+rnw0il+useGBWFHJ1ImRPLuqiuwBHjZzKab2VVmVrOE4xIJz+oZMO5maHESnHrPoe0rNg7O\nexXqt4fRl+qfz65N8Nogv+be/smg5uCJSDFYm5JWpNvl4Azo3IjdGdl8tSg57FAKFp8Ax10HN87x\nTWR+ngzPHwfvXQlbVoQdnUioCk0EnXM7nHMvOeeOA24D7gLWmdlrZta6xCMUKU07NsA7F/mSxPP+\nVzwtqHOWlYiv6peV2Lnx0PdZHqWsglf6w8aFvrPqkGf9CCDmvw56SqWYInLIGiclFOl2OThHt6hD\n3eqVGD9vXdihRKZyDTjprz4hPP5GWDgWnukJY2+CbWvCjk4kFIUmgmYWa2aDzewD4AngUaAlMBYY\nX8LxiZSezHQYdTHs3gbD3vKLaBeXxKYwfCSkbvLLSqSnFt++y4ONi+C/p/sk+OIPoF1/n/TdPB/u\nTvFflQSKSDG4tV874mP37RKZEB/Lrf3ahRRRxRQbY/Tr2JAvF20kLb0ctZKoWhtOuwdunO1LR2e9\nCU8d6fsC7NoUdnQipSqiOYLAEOBh59yRzrnHnHMbnHPvAp+WbHgipeiTv8Kq7/xIVcPOxb//xkfC\nuS/DmpnwwdWQnUd3zIpo9Qx4tT+4LLjsY2h+XNgRiUgFdtaRTejaNBEzMKBJUgL/OqczZx3ZJOzQ\nKpyBnRuRlpHFV4vLYaVLjYYw4GG4/kfo/DuY/hw82RW+vN9/ICwSBSJJBLs4565wzn27/x3OuRtK\nICaR0jfjFfjxVTjhZuh0Tskd54iBfimJhWNg4t0ld5yyYvmX8NpgqJIIl39aMgm2iEguzjl+3ZLG\nwM6N+PnBgUy9/RQlgSXEdw+txMflpTw0L7Waw1nPwbXTofWpMPkheKILTHk8+qp3JOpEkgg+a2ZJ\nOVfMrJaZvVKCMYmUrpXTYfxf/T+AU/6v5I937J98OcrUJ+HH/5X88cIy/30YMRRqt/DrMNZuGXZE\nIhIFFq7bQfKOPfRuWy/sUCq8uNgYXx66sJyVh+alXjsY+hpcPRma9YIv7oanusF3L0LmnrCjEykR\nkY4IpuRccc5tBY4suZBEStH2tfDOxZDUzJdtFtdi5AUxgzMehlZ9YdyfYflXJX/M0vbDy/Du5dC0\nB1z6sS/BEREpBZOX+i6WvdsoESwNOeWhX5fH8tC8NOoKF46Gyz71awB/cis83QNmjYCsMrpUhshB\niiQRjDGzWjlXzKw2ESxEL1LmZez2HUIzUn1zmIRahT+muMTG+a6k9Y6AUb/3zVQqAudg0kN+3cS2\n/eCi9yEhqfDHiYgUk8lLkmnXoAYNE6uEHUpU6NWiNnWqlfPy0Lw0PxYuGw8XvecbzHx0LTx/LPz0\nQfTM8ZcKL5JE8FFgmpndZ2b/BL4FHirZsERKmHPw8Z9hzY9w9n/8On+lrUrNYFmJKvDWeeV/WYns\nbPj0dvjqfugyDM5/EypVDTsqEYkiqemZzPhlK73b1g07lKgRFxtDv04NmVgRykP3Z+anjVz1NQx9\nAyzGrwn84kmw5DP/XkKkHItkHcHXgXOBDcB64Bzn3BslHZhIifr+JZg9Ak66DdoPCi+OpGZ+Tb2d\nyfD2BZBRThc8zsrwnVC/+w8c8yc463mIjQ87KhGJMtNXbCY9K1vzA0tZhSsP3Z8ZdBgMf/wWzn7B\ndxV96zy/Nu4vU8OOTuSgRTIiiHPuJ2AUMAbYaWaHlWhUIiXp52/8yFXbM+Ck28OOBpp0h3Nf8qOT\nH1xT/kpO0lNh5HCYNwr63gn97oeYiP60iIgUq8lLNlElPoaehxfjOrBSqKNb1KZ2RSwP3V9MLHQd\nBtfNgIGPwtZf4H8D4I2z/dJQIuVMJAvKDzazpcDPwCTgF+CTSHZuZv3NbLGZLTOzA95xm9mfzWyB\nmc01s4lm1jzXfVlmNju4jIn4GYkUJGUVjP491GkF57xYdhKW9oPgtHthwYfw5X1hRxO5tK3wxlmw\n9HM48wk48Rb/yamISAgmL0nm6BZ1qBJfCo2/5De/dQ9dtJHdGRWsPDQvcZWg5x/8ovSn3QdrZ8NL\nfWDkhbBxIcwdBY93gruT/Ne5o8KOWCRPkbwLvg84BljinGsB9AWmF/YgM4sFngXOADoAF5hZh/02\nmwX0cM51Ad5l37mHac65bsFlcARxihQsZ+QqK8M3h6lSM+yI9nXc9XDUpTDlMZhZDqqvt6+DVwfA\n2lm+8U2Py8KOSESi2KotqazYtEtloSEZ2LkRqekVuDw0L/EJcPwNcOMcOPkOWDEJnjvGV/dsWwU4\n/3XsDUoGpUyKJBHMcM5txncPjXHOfQX0iOBxvYBlzrkVzrl0YCQwJPcGzrmvnHM5q3VOB5oWIXaR\nyDkHY2+E9fP8MhF124Qd0YHMYMAj0LIPjLsJVnwddkT527wcXjkdtv4Kw0dBx7PCjkhEolzOshEn\nKREMxTEtc8pD14cdSumrUhNOvh1umguVa4Dbb1Q0Iw0m3htObCIFiCQRTDGz6sBkYISZPQnsiuBx\nTYBVua6vDm7LzxXsW3JaxcxmmNl0M8vzXaaZXRVsMyM5OTmCkCRqTXvWz2E75e9+WYOyKjbeL2hb\npw28cwkkLw47ogOtmwOv9IM9O+HSsdCqT9gRiYgweUkyTZISaFWvWtihRKWc8tCJCzdER3loXqrW\n9v8b87JtdenGIhKBSBLBIUAqcDPwKbAcKNY2i2Z2EX6U8eFcNzd3zvUAhgNPmFmr/R/nnHvROdfD\nOdejXj19Aij5WP4VfP5/0H4wnPiXsKMpXJVEv6xEXGUYcZ7vKFpW/DIV/ncmxFaCyydAk6PCjkhE\nhIysbL5dtpnebetimqccmr3loWXo/1ZpS8ynuC2/20VCVGAiGMzzG+ecy3bOZTrnXnPOPRWUihZm\nDdAs1/WmwW37H+NU4O/AYOfcnpzbnXNrgq8rgK+BIyM4psi+tvwM717mF24/6/ny08ikVvNgWYmN\nfl5jxu6wI4JF4+HNc6BGQ7jiM6jXNuyIREQAmL0qhR17MundRh8KhymnPHR8Re8eWpC+d/q5g7lZ\njL9dpIwpMBF0zmUB2WaWeBD7/gFoY2YtzKwSMAy//MRvzOxI4AV8Ergx1+21zKxy8H1d4HhgwUHE\nINEsfZfv4OWyYdgIqFw97IiKpulRcM4LsPp7+PCP4S4rMWsEvHMR1O8Al32qTzZFpEyZvCSZ2Bjj\nuNZaSD5Mvjy0AV9Ec3lol6Ew6ClIbAYYVEny70Oy0sOOTOQAcRFssxOYZ2afk2tuoHPuhoIe5JzL\nNLPrgAlALPCKc+4nM7sXmOGcG4MvBa0OjA5KOVYGHULbAy+YWTY+WX3QOadEUCLnHHx4LSQvhAtH\nQ+2WYUd0cDoMgVPvgS/u8s+h7/+VfgzfPg2f/QNangznv+knwouIlCGTlyTTrVkSiQnxYYcS9QZ0\nbsTb36/i68XJ9O/UMOxwwtFlqL+A/xD3tTPh079Bq1OgZuNwYxPJJZJE8P3gUmTOufHA+P1uuzPX\n96fm87hvgc4Hc0wRAKY87tfkO+1eaJ3naVZ+HH8jbFkO3zzik8EjLyyd4zoHX9wNU5/wCek5L/l5\niyIiZciWXenMXbONm/qqXL0sOLZlHWpVjWf8vHXRmwjmFhMDg5+G54+HcTf7aR/lZZqKVHiFJoLO\nuddKIxCRYrP0c9+mudO5cFyBA9flgxkMfAxSVvolMJKaQYveJXvMrEy/hMWsN+Coy2DgoxCjBZpF\npOyZsmwTzkHvtioLLQtyuoeOnbOW3RlZVInX/w7qtIJT/gGf/R3mjd47WigSskK7hprZz2a2Yv9L\naQQnUmSbl8O7V0DDTjD4mYrzqVtsPJz3mv9n8s5FkLyk5I6VsRtG/94ngb1vhTMfVxIoImXW5CXJ\nJFWNp0vTpLBDkcCAzo3YlZ7FpCVR3D10f8f8EZr2gk/+Cjs2hB2NCBDZ8hE9gJ7B5UTgKeDNkgxK\n5KDs2eE7bMbEwvkjoFLVsCMqXglJflmJmHh46zzYtan4j7F7O4z4HSwaB/0f9J9gVpRkWkQqHOcc\n3yxN5vjWdYmN0d+qsuLYVnVICspDJRATC0OehfRUGH+Ln34hErJCE0Hn3OZclzXOuSeAgaUQm0jk\nsrPhg2tg01I4739++YWKqNbhfn7BjvXFv6zEzmQ/of3Xb+HsF/2nlyIiZdjiDTvYsH0PJ2nZiDIl\nPjaG/h0bMnHhxujtHpqXem2hzx2wcCz89EHY0YhEVBraPdelh5ldQ2RNZkRKzzeP+FGsfvdDy5PC\njqZkNesJZ78Aq76Dj/5UPJ8qpqyEV/tD8mK44G3oev6h71NEpIRNDkoPT9T8wDJnQOdG7NyT+dvv\nSALHXg+Nu8P4v5RMZY9IEURSGvporsu/gO6AZrlK2bFoPHx1P3S9AI6+JuxoSkfHs6DvXTD/Xfjq\ngUPb18aF8N9+fkTw4g+hbb/iiVFEpIRNXrKJtg2q0ygxofCNpVSpPDQfsXG+RHT3dj9fUCREkXQN\n7VMagYgclOQl8P5V0PhI39QkmuaznXAzbFkBkx/yy0p0u6Do+1j1g58TGFcZLhvvm+yIiJQDaelZ\nfP/LFi45poJOBSjn4mNj6NehIR/PW6fuoftr0AFO+qv/ELvjOdD+zLAjkigVSWnoA2aWlOt6LTP7\nZ8mGJRKB3dtg5AUQX8UvdB4fZZ8Im/nkt0VvGHM9/DKlaI9f9gW8Ptg3obl8gpJAESlXpv+8mfTM\nbHq31fzAsmpAF5WH5uuEm6FhZ7+2YOqWsKORKBVJaegZzrmUnCvOua3AgJILSSQC2dnw3pWw9RcY\n+jokNg07onDExvvnX7sFjLwQNi2L7HHz34O3hkHtVnD5Z/7xIiLlyOQlyVSOi6FXi9phhyL5OE7l\nofmLjYchz0HaFvj0jrCjkSgVSSIYa2aVc66YWQJQuYDtBWDuKHi8E9yd5L/OHRV2RBXL1w/A0glw\nxr+h+XFhRxOuhFowfBTExAXLSmwuePvvX/JrLTbtCZeOgxoNSidOEZFiNHlJMke3rKOSwzIsPjaG\n0zs04At1D81boy5wwp9h7khYMiHsaCQKRZIIjgAmmtkVZnYF8DnwWsmGVc7NHQVjb4BtqwDnv469\nQclgcVnwEUx+GLpfAj2uCDuasqF2C9/tc9saeOdCyNxz4DbOwdf/9p3K2vaHi9/3ZaEiIuXMmpQ0\nlifvoncbdQst63K6h36zVB0y89T7VqjfAcbeCGkphW8vUowiWUfw38A/gfbB5T7n3EMlHVi5NvFe\nyEjb97aMNH+7HJoNC+CDP/rRrAGPRFdzmMI06wVnPw8rpx24rER2tu9O9vUDvrvq+W9E35xKEakw\ncuacnaT5gWXe8a3rkpig8tB8xVXyXUR3boDP/hF2NBJlCu0aamYtgK+dc58G1xPM7HDn3C8lHVy5\ntW110W6XyKRu8c1hKleHoW/4Tpeyr07nwpaf4cv7/GLz62b78y4+ATJS4djr4LT7ICaSYgARkbJp\n8pJkGiVWoXX96mGHIoXIKQ/9dP569mRmUTlOpbwHaNIdjrsBpj4BHc+G1n3DjkiiRCTvBkcD2bmu\nZwW3SX7ybVzi/Kjgnh2lGk6FkJ0F713hSx/PfxNqNgo7orLrxFvgsONg0di95ckZqRATD426KgkU\nkXItMyubKcs20btNPUxVIeXCwC6N2LEnk2+WqDw0XyffAXXb+hJRvU+UUhLJO8I451x6zpXg+0ol\nF1IF0PfOA8vu4qpAs2Pgm0fh6aNg1pu+XE8iM/EeWP4lDHzUl0BK/sxg28oDb8/OUHmyiJR7c1an\nsGN3ppaNKEdUHhqB+Cq+RHTbavj8zrCjkSgRSSKYbGaDc66Y2RBAH+kUpMtQGPQUJDYDzH8d/DRc\nMQH+MBGSDvNzuF46GX79Nuxoy67fOq8mwtQnocVJcNTvw46qfNi2Jp/bVZ4sIuXbpCWbiDE4obUa\nxZQXOeWhny/YwJ5MdQ/NV7NecOyfYMYrsGJS2NFIFIgkEbwG+JuZrTSzVcBtwNUlG1YF0GUo3Dwf\n7k7xX7sM9bc37QFXfA7n/hd2bYJXz4DRl8LWX0MNt8zZp/NqYPX36rwaqfzKk6N1vUURqTAmL0mm\na7MkEqvGhx2KFMGAoDx0irqHFqzP36F2SxhzPezZGXY0UsFF0jV0uXPuGKAD0N45d5xzLsJVqyVP\nZtD5d3DdDDj5b7D4U3imZzB/UC96wJeCqvPqwcurPDk+wd8uIlJObd2VzpzVKfRuo7LQ8ub4VnWp\nWSWOj+eqPLRAlar6EtGUX33jN5ESFFHXCDMbCFwL/NnM7jQzvZssDpWqwsm3wfU/QsezgvmD3WHW\niOidP7hzI0x6WJ1XD1Ve5cmDnto7Mi0iUg5NWbYJ59D8wHKoUlwMp3dsqPLQSDQ/DnpdBd+9AL9O\nCzsaqcAKTQTN7D/A+cD1gAHnAc1LOK7oktgEznkx1/zBa+GlPtHz4ncOVs+A966ExzrAV//Mf2kI\nlTZGLr/yZBGRcmrykmRqVomja9PEsEORgzCws8pDI9b3rr09JdJTw45GKqhIRgSPc85dAmx1zt0D\nHAu0LdmwolTO/MFzXoZdyfBqfz9/MCWPDpAVQcZumP2WT3pf7guLP4GeV/iS2cHPqLRRRER+45xj\n8tJkTmhTl7hYLYNTHh3fOigPVffQwlWu7hsNblkOX90fdjRSQRW6oDyQM1Er1cwaA5sBLeJWUsyg\ny3lwxED49imY8gQsGg/HXQ8n3Oz/MJR3Kat8R6yZr0HqZqjbDgY8Al2HQeUafpu6bfzXiff6ctDE\npj4J1KiWiEhUWrJhJxu279H8wHKsUlwMp3VoyGcLtLh8RFqeBEddBtOfgw5nQbOeYUckFUwkieA4\nM0sCHgZmAg54qUSjkmD+4O1w5MXwxd3wzSN+7cFT74Iuw8rfouDOwc+T4fsXYfF4f1u7Ab4GvkVv\nnwDvr8tQJX4iIgL4slDQ/MDy7swujXhv5mqmLtvEKUc0CDucsu+0e2Hp537a0NXf+PUGRYpJJF1D\n73POpTjn3sPPDTzCOaf6vNKS2ATOfQmu+MKPin34x/I1f3DPTvjhZXjuGHh9sF838fgb4cY5MGyE\n/7QrryRQREQkl8lLk2ldvzqNkxIK31jKrN/KQ+euDzuU8qFKTRj8JGxaApP+HXY0UsEUaVjJObfH\nObetpIKRAjTrGcwffMl31ny1P4y+rOzOH9y0FD65DR5rDx/fAnFV4Kzn4c8L4dS7/QRoERGRCKSl\nZ/Hdz1tUFloB5JSHfr5gPemZUdohvahanwrdLoKpT8LaWWFHIxVIOasvjHIxMb5U8voZcNLtvrnK\nMz3hy3+WjfUHs7N8TG+cDc/0gB/+C+3O8N1Qr/oaug1XSYOIiBTZdz9vJj0zm95t64YdihSDgV0a\nsn13JlOXqXtoxPrdD9Xrw4d/gsz0sKORCkKJYHlUqRr0ucMnhO0Hw+SH4emjfAfOMNYfTN0CU5+C\np46Et4fBxkXQ5x/w5wV+WYymPVT+KSIiB23ykk1Uiovh6BZ1wg5FisEJretRo0oc47S4fOQSkuDM\nJ2DjT75vhEgxyLdZjJl1L+iBzrmZxR+OFEliUz9/sNdV8Ontfv7g9y9C/wfhsGNK/vjr5vrjzRsN\nmbuh+fF+UvMRAyE2vuSPLyIiUWHy0mSOblGbhErqMlkR+PLQBkF5aGcqxWlcIiLt+kOX8+GbR+GI\nM6FRl7AjknKuoK6hjxZwnwNOKeZY5GDlzB+c/y58fhe80g86nVsyc/GyMmDhGPj+JVg5DeKr+mUf\nel4JDTsV77FERCTqrU1JY9nGnZzfo1nYoUgxGti5Ee/PXMPUZZvoc0T9sMMpP/o/CMu/8l1Er/xK\nH7zLIck3EXTO9SnNQOQQ5cwfPGKgL9Oc+iQs+tivP3j8TYe+/uCO9fDj/2DGq7BzPdRqAf0e8PP+\nEmoVy1MQERHZn5aNqJhOaFOXGpX94vJKBIugam048zF45yKY+gT0vjXsiKQci2QdQcysE9AB+K3T\nh3Pu9ZIKSg5BzvzB7sH6g5Mf9usP9r3LlxMUZf1B52DV9/D9C7DgI8jOhNanQa+nfQer8raWoYiI\nlDuTlybTsGYV2jY4xA80pUypHBfLaR0a8NlP60k/W+WhRdJ+EHQ8GyY95EtE67cPOyIppwp91ZnZ\nXcDTwaUP8BAwuITjkkOV2BTOfdmXjNZsDB9eAy+fAiunF/7YjDSY+Qa80BteOR2WfgG9robrZ8JF\n70Lb05UEiohIicvMymbK0k2c2KYupqZjFc7ALo1899Dl6h5aZAMegco14MNrISsz7GiknIpkRPB3\nQI2nalUAACAASURBVFdglnPuMjNrALxZsmFJsWnWyy9GP2+0HyH8bf7gPX6O38R7Ydtqnzgec60v\n+5z5OqRthfodfIeqLkP9SKOIiEgpmrN6G9t3Z6ostILKKQ8dP3cdfdqpPLRIqtWFAQ/Du5fDtGfg\nhJvCjkjKoUgSwTTnXLaZZZpZTWAjoBnb5UlMDHQ9H9qf6ecOTn0SfvoIDF/uCbBtFUy4AzDoMNh3\nIm1+vJZ9EBGR0ExekowZnNBa6wdWRL+Vhy7YwANZ2cTHqtqoSDqeA/Pfh68egHYDoF7bsCOSciaS\nV9wMM0sCXgJ+BGYC00o0KikZlapBn7/BdTMgrtLeJDC3Gg1h6Otw+AlKAkVE5P/Zu+/4rOrz/+Ov\nKyGLGSCAjLAUkako4EBwIbhAtFRt0Wr5tlpbV1u1+mulaOtXHK0Vta1VqW2tRasWAfUL7jiwioKg\nyJ5hJkCAQICM6/fHOQlJTEhCxn0neT8fjzyS+4zPuc65P4T7ymdFVNqKDAZ2SaZ1s/hIhyK15IIB\nHdmVk6vF5Y+EGVz4e4hvCq/8BAryIx2R1DMVJoLu/mN3z3L3PwPnAle7+/drPzSpNcmpwTjAsuzZ\nUrexiIiIlGHXvly+2JDFGb3UGtiQDT827B66WIvLH5EWHeC8+yH9E/jvE5GORuqZykwWM9PMvmtm\nzdx9rbsvqovApJa16lK17SIiInXog5WZFLiWjWjoEprEMrJvB+Z8tZXc/IJIh1M/DbwMeo0O5n3Y\nvirS0Ug9Upmuob8DTgeWmNmLZjbezBIrOkmi3DmTIC6p5La4pGC7iIhIhKUtz6BFYhNOSE2OdChS\ny9Q9tJrMYMwfIDYeZt4IBUqopXIq0zX0PXf/MdATeAK4jGDCGKnPBl4GY6ZCq1TAgu9jpgbbRURE\nIsjdSVuRwbCjU2iiCUQavOG9Umiu7qHV07ITjL4X1n0I85+OdDRST1R2QfkkYAxwOXAi8LfaDErq\nyMDLlPiJiEjUWbktm8279nPj2eoW2hgkxsUysk975i7Zyr2aPfTIDboSvvoPvPFr6HUutO4e6Ygk\nylVmjOALwNfA2cBjwNHufmNtByYiIiKN03vLMwAYcawmimksLhjQkax9uXy0anukQ6m/zGDMI2Ax\nMPMmcI90RBLlKvMnl6cJkr8fufs77l7pjsdmdp6ZLTOzlWZ2Rxn7f2ZmS8xskZm9ZWbdiu272sxW\nhF9XV/aaIiIiUr+lrcjk6HbN6NK6aaRDkToy4th2QffQReoeWi3JqTDqHljzHnyuDnxyeJUZIzjH\n3au8MImZxQKPA+cDfYHvmFnfUoctAAa7+0DgReCB8Nw2wK+Bk4GhwK/NrHVVYxAREZH6ZX9uPv9d\nvV2zhTYyhd1D5yzZotlDq+vEa6D7cJjzK9iVHuloJIrVZifsocBKd1/t7geB6cDFxQ8IWxj3hS8/\nBgrXLhgNvOHuO9x9J/AGcF4txioiIiJR4JM1OziQV6BEsBEq7B46T91DqycmBsY+Cp4Ps25WF1Ep\nV20mgp2BDcVep4fbyvM/wOtVOdfMrjWz+WY2PyMjo5rhioiISKSlLc8gvkkMp/RoG+lQpI4VdQ/V\n7KHV16YHjJwMK9+Ehc9FOhqJUpWZLOatymyrDjO7EhgMPFiV89z9L+4+2N0Ht2unvxyKiIjUd2kr\nMhjavQ1J8bGRDkXqWGJcLOf0ac+cr9Q9tEYM+SF0PQ3m3Am7lVzLN5WbCJpZYjhWL8XMWptZm/Cr\nO4dv2Su0EUgt9rpLuK30dUYCvwTGuvuBqpwrIiIiDcfmXTks35qt2UIbsQsGdGSnuofWjJgYuPgx\nyDsAs3+qLqLyDYdrEbwO+Aw4Lvxe+PUKwTISFfkU6GVmPcwsHrgCmFn8ADMbRLBI/Vh3L75I/Rxg\nVJiAtgZGhdtERESkgXp/eSaAxgc2Ymcc245m8bHqHlpT2h4NZ98Fy1+HxS9GOhqJMuUmgu7+iLv3\nAG51957u3iP8Ot7dK0wE3T0PuIEggfsaeMHdvzKze8xsbHjYg0Bz4N9mttDMZobn7gB+Q5BMfgrc\nE24TERGRBuq9FRl0aJlA7w4tIh2KREjQPbSDuofWpFOuhy5D4PXbIHtbxcdLo1GZyWK2mFkLADP7\nlZm9bGYnVqZwd3/N3Y9196Pd/d5w2yR3L0z4Rrp7B3c/IfwaW+zcae5+TPj11yO4NxEREakn8guc\nD1ZkMrxXO8ws0uFIBBV2D/14tbqH1oiYWLj4cTi4D179eaSjkShSmUTwLnffY2anAyMJFpj/U+2G\nJSIiIo3JovQsduXkqluocGZvdQ+tce16w5l3wNcz4av/RDoaiRKVSQQLF5O/EPiLu78KxNdeSCIi\nItLYpC3PxAyGH6OJYhq7Q91Dt5Kn7qE157SboNMgePVW2JsZ6WgkClQmEdxoZk8AlwOvmVlCJc8T\nERERqZS0FRkM7NyK1s30t2YJuofu2HuQj1driogaE9sELv4j7NsBf+gPk5Ph4f6w6IVIRyYRUpmE\n7jKCCV9Gu3sW0Aa4rVajEhERkUZjV04uCzdkqVuoFCnsHvqquofWrK1fBstK5OYADrs2wKyblAw2\nUhUmgu6+D9gGnB5uygNW1GZQIiIi0nh8tDKT/AJXIihFEuNiOTucPVTdQ2vQW/dAQV7Jbbk5wXZp\ndCpMBM3s18AvgDvDTXHAs7UZlIiIiDQeaSsyaJHQhBNSkyMdikSRCwccpe6hNW1XetW2S4NWma6h\nlwBjgb0A7r4J0AI/IiIiUm3uTtryTE47pi1xsZqCQA45s3d7mqp7aM1q1aXs7XFJwfIS0qhU5jfu\nQXd3wAHMrFnthiQiIiKNxaqMvWzMylG3UPmGxLhYzj6uvbqH1qRzJgVJX3ExTSB3Hzw1Eravikxc\nEhGVSQRfCGcNTTazHwJvAk/WblgiIiLSGKQtzwBgRC8lgvJNF4azh/53jbqH1oiBl8GYqdAqFbDg\n+7g/wYSXYM8m+MuZsGRmpKOUOtKkogPc/SEzOxfYDfQGJrn7G7UemYiIiDR4aSsy6JnSjNQ2TSMd\nikShM3u3Jyku6B46TGtM1oyBlwVfpV33Pvz7anjhKjj1Bhg5GWLj6jo6qUOV6ozv7m+4+23AFIIW\nQREREZFq2Z+bz8ert6tbqJQrKT6Wc/q0Z86X6h5a65JT4fuvw5AfwLzH4G9jYbfGZzZk5SaCZnaK\nmb1rZi+b2SAz+xL4EthqZufVXYgiIiLSEM1fu5P9uQWMOFYtPVK+Cwd0ZPveg3yi7qG1r0kCXPg7\nuPQp2LwQnhgBa96PdFRSSw7XIvgY8L/Av4C3gR+4+1HACOC+OohNREREGrC0FRnEx8ZwSs+2kQ5F\noljx7qFSRwZ+G374NiS2gr+PhQ8eBvdIRyU17HCJYBN3n+vu/wa2uPvHAO6+tG5CExERkYYsbXkG\ng7u3pml8hVMWSCOWFB/L2X2C2UPzC5SM1Jn2feDad6DPWHhzMkyfADlZkY5KatDhEsHiHbFzSu3T\nv0IRERE5Ylt372fplj0aHyiVcuGAjmRmH+S/a7ZHOpTGJaEFfPsZOG8KrJgTzCq6eVGko5IacrhE\n8Hgz221me4CB4c+FrwfUUXwiIiLSAGnZCKmKswq7hy5S99A6ZwanXA/XvAZ5++Hpc2HBs5GOSmpA\nuYmgu8e6e0t3b+HuTcKfC19rLlkRERE5YmkrMmnXIoE+HVtEOhSpB5LiDy0ur+6hEdL15GCJidSh\n8MpP4JUbILd0p0GpTyq1fISIiIhITckvcN5fkcHwXimYWaTDkXriAnUPjbzm7eCqGTD8Vljwj6B1\ncMeaSEclR0iJoIiIiNSpxRt3kbUvlzM0PlCq4Kzj2pEYF8Nrmj00smJi4Zy74DvPQ9Z6eOIMWPpa\npKOSI6BEUEREROpU2vIMzOD0Y7R+oFRe0/gmnHNcB/7vy63qHhoNep8H16VBm+4w/TvBzKL5eZGO\nSqpAiaCIiIjUqbTlGfTv1Iq2zRMiHYrUM0H30ANaXD5atO4OE+fCSdcEaw3+Yxxkb4t0VFJJSgRF\nRESkzuzen8uCDVmMOFatgVJ16h4aheISYcwjMO5PkP4p/Hk4rJsX6aikEpQIioiISJ35aGUm+QWu\nZSPkiDSNb8LZx7Xn9S81e2jUOeG78IO3IL4pPHMhfPQYuN6jaKZEUEREROrMe8szaZ7QhBO7tY50\nKFJPtWueQGb2AY75f68xbMrbzFiwMdIhSaGj+sO170Lv82HuL+GF78H+3ZGOSsqhRFBERETqhLuT\ntjyDU49uS1ysPoJI1c1YsJHn528AwIGNWTnc+fJiJYPRJLEVXP4sjPotLH0V/nImbP0q0lFJGfRb\nWEREROrE6sy9bMzKYYSWjZAj9OCcZezPLSixLSc3nwfnLItQRFImMzjtRrh6FhzMhifPgS+mRzoq\nKUWJoIiIiNSJtOUZAJyh8YFyhDZl5VRpu0RY92Fw3fvQ+UT4z3Uw+6eQdyDSUUlIiaCIiIjUibTl\nGXRv25SubZtGOhSppzolJ5W53SzoNuqanCT6tOgA35sJw26G+dNg2mjYuS7SUQlKBEVERKQOHMjL\n5+PVO9QtVKrlttG9SYqLLbEtoUkMqa2TuOX5hUx85lO1Dkaj2CZw7j1wxXOwfTU8MQKWz410VI2e\nEkERERGpdfPX7iQnN1/LRki1jBvUmfsuHUDn5CQM6JycxP3fGsjbt57FpIv68vHqHZz7+/f4+7y1\nFGh5iehz3IVw3bvQKhWe+za8fS8U5Ec6qkbLGkoT+uDBg33+/PmRDkNERETKcN9rXzPtwzUsnDSK\nZglNIh2ONFAbduzj//1nMe+vyGRI99bcd+lAjmnfPNJhSWm5OfDqrbDwWeh5FnzrKWiWEumoGgQz\n+8zdB1fmWLUIioiISK17b3kGJ3VrrSRQalVqm6b8feJQHvr28Szfms0Fj7zP4++sJDe/oOKTpe7E\nJcG4x2Hso7Duo6Cr6IZPIx1Vo6NEUERERGrVtt37Wbplj8YHSp0wM8af1IU3fjaCc/t24ME5yxj7\n2IcsTt8V6dCktBO/Bz94A2Lj4K/nw3+fgAbSW7E+UCIoIiIitSptRSaAxgdKnWrfIpHHJ5zIE1ed\nxPbsA1z8+Afc99rX5BzUmLSo0vF4uPZdOGYkvH47PH0u/L4vTE6Gh/vDohciHWGDpURQREREalXa\n8gxSmsfTt2PLSIcijdDofkfxxs/O4PIhqTyRtprzH0lj3qrtkQ5LiktqHcwo2vdSSP8Udm8EHHZt\ngFk3KRmsJUoERUREpNYUFDgfrMxkeK92xMRYpMORRqpVUhz3XTqQ5354Mg5858mPufPlxezenxvp\n0KRQTAxsLGOcYG4OvHZbMIYwV0uD1CSN2BYREZFa8+WmXezYe5ARx2pGQIm8045O4f9uHsHDby7n\nqfdX8/bSrfx23ADO7dsh0qEJwK70srfvz4KnR4LFQrvjoNMJQZfSjifAUQMgvmndxtlAKBEUERGR\nWpO2PAOA4RofKFEiKT6W/3dBHy4a2JHbX1zED/8+nwsHdmTymH60a5EQ6fAat1Zdgu6gpbXoCBc8\nBJsXwqaFsGIuLPxnsM9iIKV3mByeEHw/agDEN6vb2OshrSMoIiIiteayP89jX24es28cHulQRL4h\nN7+AJ95bxdS3VtI0IZa7LuzLpSd2xkzdmCNi0QvBmMDiXUDjkmDMVBh42aFt7rB706HEsPD73m3B\nfouBlGMPtRp2OgGOGggJ1V9TcsaCjTw4ZxmbsnLolJzEbaN7M25Q52qXW1Oqso6gEkERERGpFXv2\n5zLonje4dkRPbj/vuEiHI1Kuldv2cMdLi5m/bicjjm3H/17Sny6t1d0wIha9AG/dE3QTbdUFzplU\nMgksjzvs2QybvyiZHGZvCQ8wSOl1KDHseAJ0HAgJLSod2owFG7nz5cXk5B6aeTYpLpb7Lh0QNclg\n1CSCZnYe8AgQCzzl7lNK7R8B/AEYCFzh7i8W25cPLA5frnf3sYe7lhJBERGR6DLnqy1c94/PmH7t\nKZzSs22kwxE5rIIC59n/ruP+15fiwO2je3PVqd2J1SRH9dueLSUTw80Lg4QRAIO2xwQth0XJ4fGQ\nWHKG49z8ApZu3sOVT/+XXTnfnGCoc3ISH95xdh3cTMWqkgjW2hhBM4sFHgfOBdKBT81sprsvKXbY\neuAa4NYyishx9xNqKz4RERGpXWnLM2gWH8uJXVtHOhSRCsXEGN87tTvn9OnAL/+zmMmzljDzi03c\n/62B9OpQ+VYjiTItjoLe5wVfhfZsDRLCwtbD9fPgy6L2KPJb9ySjeR++tp68n92FWRntycgNxo+O\njfmA25u8QCfLZJOn8EDeZczKOr2u76pG1OZkMUOBle6+GsDMpgMXA0WJoLuvDfcV1GIcIiIiUsfc\nnbQVGZx6dArxTbRaldQfnZOT+Os1Q5ixcCP3zFrChVM/4CdnHcP1Zx6tutxQtOgALUbDsaPJL3CW\nbdnDkhUr2bHyE2K3fEGXzOX03/ExZ9mrnAVMioXsll1ZszeB3r6aeAu6hnaxTKbEPUWbuHjgwoje\n0pGozUSwM1B82p904OQqnJ9oZvOBPGCKu8+oyeBERESk9qzdvo8NO3K4dnjPSIciUmVmxiWDujC8\nVzvunrWEh99czmuLN3P/+IGckJoc6fCkGrL2HWTB+iw+X7+Tz9bt5IsNWew9GCR2Kc2PZlDXwZzU\nrTWtu7amTXIuSZmLYfNCmm9eSL+lrxFDfonymtpBbo97Hrg7AndTPdG8fEQ3d99oZj2Bt81ssbuv\nKn6AmV0LXAvQtWvXSMQoIiIiZShcNmLEsVo2QuqvlOYJPPqdQVx8fCd+NeNLLv3jh0wc1oOfjTqW\npvHR/DFaIBj3uWJbNp+v38nn63by+fqdrMrYC0BsjHHcUS249MQunNStNSd2bU1qm6RvzhjbugP0\nGglAzOSy/wjQNGdLmdujXW3W4I1AarHXXcJtleLuG8Pvq83sXWAQsKrUMX8B/gLBZDHVjFdERERq\nSNryDLq1bUq3tlrLS+q/kX07MLRnG+5/fSlPfbCGOUu2MOXSgQw7JiXSoUkxu/fnsnB9Fp+FSd/C\nDVns2Z8HQOumcZzYtTWXntiFE7u25vjUVlVP5stb57BVlxqIvu7VZiL4KdDLzHoQJIBXAN+tzIlm\n1hrY5+4HzCwFGAY8UGuRioiISI05mFfAvNXb+daJ9fPDkUhZWibGce8lAxhzfCfufHkxE576L5cN\n7sIvL+hLq6ZxkQ6v0qJ9HbzKKihwVmfuLdHat2JbNu5gBr07tGDM8Z04sWtrTurWmu5tm1Z/fchz\nJpW9zuE5k6pXboTUWiLo7nlmdgMwh2D5iGnu/pWZ3QPMd/eZZjYE+A/QGhhjZne7ez+gD/BEOIlM\nDMEYwSXlXEpERESiyPx1O9h3MF/dQqVBOqVnW16/eTiPvLWCv6St5p1lGfzm4n6c179jpEOrUOl1\n8DZm5XDHy4vwAueSk6LjDzflJarZB/L4YkNWUdL3+fqsoqUcWiY24cRurbloYKei1r4WibWQnBeu\nZ3gk6xxGIS0oLyIiIjVqyutLeer91Sz89SiaJ2gclTRcX27cxS9eWsRXm3Zzfv+juPvifrRvkVhn\n13d39hzII2tvLjv2HWTnvoNk7TvIzr25wfd9ueG24PvSzXvIL+ezf2yMERdrxMfGEN8khrji32Nj\niGsSQ3yslbGt8GcrOie+2P5D2w6dW/y4Q9cxPliRwe/fWMGBvIIScXVokcCW3fspCEPv1b55UUvf\nid2S6ZnSnBit9whEyTqCIiIi0jilLc/gpG6tlQRKg9e/cytm/GQYT72/hoffXM6HKzO5YGBH3l+e\nwaas/VXqepmbX0DWvtIJ3EF2FCV1wfasYt+z9uWSV1B2YmcGrZLiaN00nuSmcXRomchXm3aXe/0f\nndGTg3kF5OY7B/MLwp+Dr4N5BRzMd3LzCtifW8Ce/XnhtvCYvOCc3HDbwfwCaqqtKb/A2b73IDec\n3YuTurXmhNRkWiXVn6640Uy/oUVERKTGZOw5wJLNu7ltdO9IhyJSJ+JiY7j+zKMZ3a8DP/jbfKZ/\ncmgykY1ZOdz+4iI+W7+TXu2bs3NvsQRv36EEL2tvLnsO5JV7jfgmMbRueiip69W+OclN40tsa9Ms\nvsS2lklxxJZqJRs25W02ZuV8o/zOyUncNvq4Gnsm7k5+gQdJZbHkMDdMLg8UJZlelHAezC/gun98\nVmZ5B/MK+Nm5x9ZYfBJQIigiIiI15v0VwbIRZ2h8oDQyPds1Z39e/je2H8wv4B/z1hW9bpHYhNbF\nEraeKc3CBC6e1s3iwn1Bcte6WXBcUlxs9Sc6AW4b3bvEGEGApLjYGv/DjZnRJNZoEgtJ8bGVPq9z\nclKZiWqn5KSaDE9CSgRFRESkxqQtz6Bts3j6dmwZ6VBE6tzmrP1lbjfg01+NJDkpjiaxMXUbVDGF\nXVSjddbQukpUJaBEUERERGpEQYHz/opMhvdK0cQN0ih1OkyLVkrzhAhE9E3jBnWOmsSvtGhPVBsa\nJYIiIiJSI5Zs3s32vQe1bIQ0WmrRqr5oTlQbGiWCIiIiUiPeWx6MDxzeS4mgNE5q0ZL6RImgiIiI\n1Ii05Rn07diSdi2iowucSCSoRUvqi8iNVhUREZEGI/tAHp+t26luoSIi9YQSQREREam2eau2k1fg\njDg2JdKhiIhIJSgRFBERkWpLW55B0/hYBndrE+lQRESkEpQIioiISLWlrcjg1J5tiW+ijxYiIvWB\nfluLiIhItazbvpd12/dpfKCISD2iRFBERESqJS1cNkKJoIhI/aFEUERERKrlveWZpLZJonvbppEO\nRUREKkmJoIiIiByxg3kFzFuVyYhe7TCzSIcjIiKVpERQREREjtjn63ey92C+uoWKiNQzSgRFRETk\niKUtz6BJjHHa0W0jHYqIiFSBEkERERE5Yu8tz+DErq1pkRgX6VBERKQKmkQ6ABEREamfMvYc4KtN\nu7l11LGRDkWkUnJzc0lPT2f//v2RDkWkWhITE+nSpQtxcUf+RzglgiIiInJEPlipZSOkfklPT6dF\nixZ0795dkxtJveXubN++nfT0dHr06HHE5ahrqIiIiByRtOWZtGkWT/9OrSIdikil7N+/n7Zt2yoJ\nlHrNzGjbtm21W7aVCIqIiEiVFRQ476/I4PRjUoiJ0YdqqT+UBEpDUBP1WF1Da8mMBRt5cM4yNmXl\n0Ck5idtG92bcoM6RDquEaI9R8VWP4qsexdewRfvzqw/x3fva12RmHyRtRQYzFmyMqvhERKRi5u6R\njqFGDB482OfPnx/pMIDgP8g7X15MTm5+0baEJjHccNYxnNm7fQQjO+TdZdt47J2VHMgrKNoWTTEq\nvupRfNVTH+NLiovlvksH6MN4JcxYsJE7Xl7E/tz68/5Ge3yqf1JffP311/Tp0yeiMaxdu5aLLrqI\nL7/8ssbLfvfdd3nooYeYPXs2M2fOZMmSJdxxxx01fp36oKrP+ZlnnmHUqFF06tTpsMfMnz+fxx57\nrKbCrJay6rOZfebugytzvhLBWjBsyttszMqJdBgi0sgkxcXwg+E96ZHSjB4pzeiZ0pxWTRvvlP7u\nzrY9B1idsZfVmdmsydjLmsy9vLc8g7yChvF/XzTpnJzEh3ecHekwRA6rqolgbbTO11UiWK8segHe\nugd2pUOrLnDOJBh4WbWKrOpzPvPMM3nooYcYPLj8HKq2EsG8vDyaNKl6R83qJoLqGloLNh0mCXzq\ne5V6X2rdD/5eftIcDTEqvupRfNVTX+PLyS3gj++uIr9YktOmWTw9w8SwR7tm4c/N6da2KYlxsXUV\ncq3avT+3KMlbnZHN6szg5zWZe9l3sGTPjB4pzQ6bBEbz+wvRHd/h/u8TqY9K9/DamJXDnS8vBqh2\nMpiXl8eECRP4/PPP6devH3//+9956KGHmDVrFjk5OZx22mk88cQTmBlTp07lz3/+M02aNKFv375M\nnz6dvXv3cuONN/Lll1+Sm5vL5MmTufjii0tco3jScs0119CyZUvmz5/Pli1beOCBBxg/fjwADz74\nIC+88AIHDhzgkksu4e67767WvVXZohdg1k2QG/4O2bUheA3VTgYr+5xfeukl5s+fz4QJE0hKSmLe\nvHl8+eWX3Hzzzezdu5eEhATeeustADZt2sR5553HqlWruOSSS3jggQcAaN68OTfffDOzZ88mKSmJ\nV155hQ4dOrB27VomTpxIZmYm7dq1469//Stdu3blmmuuITExkQULFjBs2DBatmzJmjVrWL16NevX\nr+fhhx/m448/5vXXX6dz587MmjWrWktFlEWJYC3olJxUZotg5+QkRvbtEIGIvqlzlMeo+KpH8VVP\nfY7vnVvPZMPOfawpbAXL3MvqjKAV7N+fpRcdaxYcH7QcFiaKzemZ0oxOyUnERtnkHwfy8lm/fd+h\nJK/Y/WVmHyw6LsagS+um9GzXjKE92hQlvj3aNaNjy0RiYqzcXhv14f2N5vg6JSdFIBqRI3f3rK9Y\nsml3ufsXrM/iYH5BiW05ufnc/uIi/vXJ+jLP6dupJb8e06/Cay9btoynn36aYcOGMXHiRP74xz9y\nww03MGnSJACuuuoqZs+ezZgxY5gyZQpr1qwhISGBrKwsAO69917OPvtspk2bRlZWFkOHDmXkyJGH\nvebmzZv54IMPWLp0KWPHjmX8+PHMnTuXFStW8Mknn+DujB07lrS0NEaMGFHhPVTa63fAlsXl70//\nFPIPlNyWmwOv3ACf/a3sc44aAOdPqfDSlX3O48eP57HHHitqETx48CCXX345zz//PEOGDGH37t0k\nJQW/4xYuXMiCBQtISEigd+/e3HjjjaSmprJ3715OOeUU7r33Xm6//XaefPJJfvWrX3HjjTdy9dVX\nc/XVVzNt2jRuuukmZsyYEdx6ejofffQRsbGxTJ48mVWrVvHOO++wZMkSTj31VF566SUeeOABBC6W\nkgAAIABJREFULrnkEl599VXGjRtX8fOuAiWCteC20b2/MUYwKS6W20b3jmBUJUV7jIqvehRf9dTn\n+OKbxHB0u+Yc3a45UDJpyD6QVyKBKvx66fONZB/IKzouvkkM3ds2DbuYBslhz3ZBstimWXytzbhX\nUOBs2pVTFNfqwla+zGw27syheENeSvMEeqY045zjOhTF1rNdM1LbNCWhyeFbOuvz+xsNoj0+kZpS\nOgmsaHtVpKamMmzYMACuvPJKpk6dSo8ePXjggQfYt28fO3bsoF+/fowZM4aBAwcyYcIExo0bV5QI\nzJ07l5kzZ/LQQw8BwbIY69eXnZwWGjduHDExMfTt25etW7cWlTN37lwGDRoEQHZ2NitWrKjZRLAi\npZPAirZXQVWec3HLli2jY8eODBkyBICWLVsW7TvnnHNo1SpYMqdv376sW7eO1NRU4uPjueiiiwA4\n6aSTeOONNwCYN28eL7/8MhAknrfffntRWd/+9reJjT30f9b5559PXFwcAwYMID8/n/POOw+AAQMG\nsHbt2mo/j9KUCNaCwu4C0TzjW7THqPiqR/FVT0ONr3lCEwZ0acWALiXXfHN3MrIPFHWvDJKvvazc\nls3bS7eRm38oA2uZ2KSo5bBn2N20cExi0/hD/6UcblzNzr0HWR124yyekK7J3FtiApJm8bH0aNeM\nE1Jbc8mgLkUJafeUZrRMPPLuMQ31/a0r0R6fSGVV1HJ3uN4Dz193arWuXfoPambGj3/8Y+bPn09q\naiqTJ08uWiPu1VdfJS0tjVmzZnHvvfeyePFi3J2XXnqJ3r1L/gGmMMErS0JCQtHPhXOEuDt33nkn\n1113XbXu57Aqarl7uH/QHbS0Vqnw/VerdemqPOfKKv4cY2NjycsL/pAaFxdXdL3i2w+nWbNmZZYd\nExNToryYmJhKlVdVSgRrybhBnaP+P8Voj1HxVY/iq57GFJ+Z0b5FIu1bJHJyz7Yl9uXlF7AxKyfo\nklmshe6/q7fznwUbSxx7VMtEeqQ0w8z5dO3OogRyY1YOP3/hCx5+Yxm79ueRtS+36JwmMUbXtk3p\nmdKM4b1Sgm6cKc04ul0z2rVIqLXWx8b0/taGaI9PpCbUZuv3+vXrmTdvHqeeeirPPfccp59+Oh99\n9BEpKSlkZ2fz4osvMn78eAoKCtiwYQNnnXUWp59+OtOnTyc7O5vRo0fz6KOP8uijj2JmLFiwoKhV\nrypGjx7NXXfdxYQJE2jevDkbN24kLi6O9u3rcIbicyaVHCMIEJcUbK+myj5ngBYtWrBnzx4Aevfu\nzebNm/n0008ZMmQIe/bsKeoaWlWnnXYa06dP56qrruKf//wnw4cPr/Z91RQlgiIiUq4msTF0a9uM\nbm2bcVapzz45B/NZu/1Qa96qsIVv4YYsSk9Ine/Olt0HGH9Sl6JunD1SmtOldRJxsTF1d0MiIpVU\nm63fvXv35vHHH2fixIn07duX66+/np07d9K/f3+OOuqooi6J+fn5XHnllezatQt356abbiI5OZm7\n7rqLW265hYEDB1JQUECPHj2OaKbQUaNG8fXXX3PqqUELZ/PmzXn22WfrNhEsnBCmhmcNhco/Z4Br\nrrmGH/3oR0WTxTz//PPceOON5OTkkJSUxJtvvnlEMTz66KN8//vf58EHHyyaLCZaaPkIERGpUT3u\neJWy/mcxYM2UC+s6HBGRItGwjqBITanu8hH6M6yIiNSo8maP1KySIiIi0UOJoIiI1KjbRvcmqdQa\nhZpVUkREJLpojKCIiNQozSopItHM3WttIiqRulITw/uUCIqISI3TrJIiEo0SExPZvn07bdu2VTIo\n9Za7s337dhITE6tVjhJBEREREWkUunTpQnp6OhkZGZEORaRaEhMT6dKlS7XKUCIoIiIiIo1CXFwc\nPXr0iHQYIlFBk8WIiIiIiIg0MkoERUREREREGhklgiIiIiIiIo2M1cTUo9HAzDKAdRUc1grYVcki\nK3tsRcelAJmVvGZ9VpVnW19jqKnyq1tOVc+PRL2HxlH3Ve/rrqzarPeVPV71PhAN9R5qN476Wu+r\neo7qfeU1hnpfk+XX93pfmeOitd53c/d2lTrS3RvNF/CXmj62ouOA+ZG+72h7tvU1hpoqv7rlVPX8\nSNT78JgGX/dV7+uurNqs95U9XvW+5utEtMZRX+t9Vc9RvY9MnYjmOKLhs0401PvKHNcQ6n1j6xo6\nqxaOrUqZDVk0PIfajqGmyq9uOVU9X/W+9kTDc6gv9b66ZdVmva/s8dHwfkeDaHkOtRlHfa33VT1H\n9b7youU51Jff+fW93h9pHPVKg+kaGq3MbL67D450HCJ1TXVfGiPVe2mMVO+lMWoI9b6xtQhGwl8i\nHYBIhKjuS2Okei+Nkeq9NEb1vt6rRVBERERERKSRUYugiIiIiIhII6NEUEREREREpJFRIigiIiIi\nItLIKBEUERERERFpZJQIRpiZNTOz+WZ2UaRjEakLZtbHzP5sZi+a2fWRjkekrpjZODN70syeN7NR\nkY5HpC6YWU8ze9rMXox0LCK1KfxM/7fw9/yESMdTGUoEj5CZTTOzbWb2Zant55nZMjNbaWZ3VKKo\nXwAv1E6UIjWrJuq9u3/t7j8CLgOG1Wa8IjWlhur+DHf/IfAj4PLajFekJtRQvV/t7v9Tu5GK1I4q\n/hu4FHgx/D0/ts6DPQJaPuIImdkIIBv4u7v3D7fFAsuBc4F04FPgO0AscF+pIiYCxwNtgUQg091n\n1030IkemJuq9u28zs7HA9cA/3P25uopf5EjVVN0Pz/sd8E93/7yOwhc5IjVc71909/F1FbtITaji\nv4GLgdfdfaGZPefu341Q2JXWJNIB1FfunmZm3UttHgqsdPfVAGY2HbjY3e8DvtH108zOBJoBfYEc\nM3vN3QtqM26R6qiJeh+WMxOYaWavAkoEJerV0O98A6YQfFBQEihRr6Z+54vUV1X5N0CQFHYBFlJP\nel0qEaxZnYENxV6nAyeXd7C7/xLAzK4haBFUEij1UZXqffgHkEuBBOC1Wo1MpHZVqe4DNwIjgVZm\ndoy7/7k2gxOpJVX9nd8WuBcYZGZ3hgmjSH1W3r+BqcBjZnYhMCsSgVWVEsEo4O7PRDoGkbri7u8C\n70Y4DJE65+5TCT4oiDQa7r6dYFysSIPm7nuB70c6jqqoF82W9chGILXY6y7hNpGGTPVeGivVfWmM\nVO+lsWsw/waUCNasT4FeZtbDzOKBK4CZEY5JpLap3ktjpbovjZHqvTR2DebfgBLBI2Rm/wLmAb3N\nLN3M/sfd84AbgDnA18AL7v5VJOMUqUmq99JYqe5LY6R6L41dQ/83oOUjREREREREGhm1CIqIiIiI\niDQySgRFREREREQaGSWCIiIiIiIijYwSQRERERERkUZGiaCIiIiIiEgjo0RQRERERESkkVEiKCIi\nNcbMHjazW4q9nmNmTxV7/Tsz+1kFZXxUieusNbOUMrafaWanlXPOWDO7o4JyO5nZi+HPJ5jZBVU8\n/xozeyz8+Udm9r2K7qWiezjScmpDGNvsSMchIiLV1yTSAYiISIPyIXAZ8AcziwFSgJbF9p8G/PRw\nBbh7mYlcJZ0JZAPfSCbdfSYws4JrbwLGhy9PAAYDr1X2/FJl/bmyx5ZyJsXuoRrliIiIlEstgiIi\nUpM+Ak4Nf+4HfAnsMbPWZpYA9AE+BzCz28zsUzNbZGZ3FxZgZtnh9xgz+6OZLTWzN8zsNTMbX+xa\nN5rZ52a22MyOM7PuwI+An5rZQjMbXjywUq11z5jZVDP7yMxWF5ZrZt3N7EsziwfuAS4Py7q81Plj\nzOy/ZrbAzN40sw6lH4SZTTazW8NWxoXFvvLNrFtZZZR1D4XlhGWeYGYfh8/sP2bWOtz+rpndb2af\nmNny0vceHtPRzNLCcr8sPMbMzguf4xdm9la4baiZzQtj+8jMepdRXjMzmxZec4GZXVxurRARkaij\nRFBERGpM2KKWZ2ZdCVr/5gH/JUgOBwOL3f2gmY0CegFDCVreTjKzEaWKuxToDvQFruJQglko091P\nBP4E3Orua4E/Aw+7+wnu/n4F4XYETgcuAqaUuo+DwCTg+bCs50ud+wFwirsPAqYDt5d3EXffFJZx\nAvAk8JK7ryurjErcw9+BX7j7QGAx8Oti+5q4+1DgllLbC30XmBPGcTyw0MzahTF9y92PB74dHrsU\nGB7GNgn43zLK+yXwdnjNs4AHzaxZec9BRESii7qGiohITfuIIAk8Dfg90Dn8eRdB11GAUeHXgvB1\nc4LEMK1YOacD/3b3AmCLmb1T6jovh98/I0gaq2pGWPaSslr0KtAFeN7MOgLxwJqKTjCzYcAPCe6r\nymWYWSsg2d3fCzf9Dfh3sUOKP4/uZRTxKTDNzOII7n2hmZ0JpLn7GgB33xEe2wr4m5n1AhyIK6O8\nUcDYwtZKIBHoCnx9uPsQEZHooBZBERGpaR8SJH4DCLqGfkzQmncah8buGXBfYUuZux/j7k9X8ToH\nwu/5HNkfNg8U+9mqeO6jwGPuPgC4jiAJKleY7D0NXObu2UdSRiUc9nm4exowAtgIPFPBBDS/Ad5x\n9/7AmHJiM4KWxML3sKu7KwkUEaknlAiKiEhN+4igu+UOd88PW5mSCZLBwkRwDjDRzJoDmFlnM2tf\nqpwPgW+FYwU7EEyiUpE9QIsauIeKympFkFABXH24QsIWuH8TdOlcXokyyryuu+8CdhYb/3cV8F7p\n4w4TRzdgq7s/CTwFnEiQpI8wsx7hMW3KiO2acoqcQzBO08JzB1U2FhERiTwlgiIiDYSZZZtZz0jH\nQTB2LYUgySi+bZe7ZwK4+1zgOWCemS0GXuSbyc9LQDqwBHiWYJKZXRVcexZwSVmTxRyBd4C+hZPF\nlNo3Gfi3mX0GZFZQzmkE4yPvLjZhzE/CfWWVUfoeTgAKj7+aYCzeonD7PRXdhJl1DSfgOQv4wswW\nAJcDj7h7BnAt8LKZfQG8Eh77EHBfeGzp1sXzwnr2G4Iuo4vM7KvwdfHrdjczN7Mm4evXzeywSfOR\nMLOvwi6uUS2cbOiDSMchIlLI3D3SMYiIRAUzWwt0IOhal0vQevUjd99QA+X+wN3fLGf/mcCz7t6l\nOtdpiMysubtnm1lb4BNgmLtviXRcdcnMriGoP6eXs/9dgvrzVFn7q3ntIy47nAF1DRDn7nk1FM8z\nQLq7/6omyqtLFb2PIiJ1TS2CIiIljXH35gQzSm4lGMcVcYWtKg1RBfc228wWAu8Dv2lsSaCIiEht\nUSIoIlIGd99P0F2xb+E2M0sws4fMbL2ZbTWzP5tZUrgvxcxmm1mWme0ws/fDsW3/IJhJcVbYdbPE\nMgPhdPuvA53C/dkWrDs32cxeNLNnzWw3cE2xtd2yzGyzmT1mwXp3hWW5mR0T/vyMmT1uZq+a2R4L\n1qs7urz7NbN/m9kWM9tlwVpz/YrtSzKz35nZunD/B8Xu+3QL1pnLMrMNYatH4bp2PyhWRolucWGs\nPzGzFcCKcNsjYRm7zewzMxvu7meGyx0MCJ/RqvB+PjOz1PAef1fqXmaa2TcWrTezP5nZQ6W2vWJm\nPwt//oWZbQzLX2Zm55RRRo/wXmPC10+a2bZi+/9hZreEP7cys6fD92qjmf3WzGLLeR6jwmvusmDt\nxPeKP7/wmIfMbKeZrTGz88Nt9wLDgcfCuvNYGTGX7qL5rpn9xsw+DO91rpmllD62vLJL1bMLLVhD\ncHf43k0uff1icRTVCQvWLMwu9uUWdu8sry6a2bXABOD28JxZ4fa1ZjYy/DnBzP5gZpvCrz9YsH4l\nZnammaWb2c/NbFv4vnz/MPFeY8Eak3vCZz6h2L4fmtnX4b4lZnZiuP2OYnV0iZldcpjyj7Ngfcwd\n4Xt/WXnHiojUBiWCIiJlMLOmBOOoio9zmwIcSzA26xiCZREmhft+TjCerR1B99L/B7i7XwWsJ2xp\ndPcHil/H3fcC5wObwv3Nw7X4AC4mSEaTgX8SdFn9KcH4u1OBc4AfH+Y2rgDuBloDK4F7D3Ps6wTL\nN7QnGIv3z2L7HgJOIhjr1oZgzbwCCyYfeZ2g1bRd+FwWHuYapY0DTuZQsv1pWEYbgvGD/zazwtkq\nfwZ8B7gAaAlMBPYRLKHwnWKJWQowMjy/tH8RLBBfOLlJa4IlEKZbsGD6DcAQd28BjAbWli4gXGZh\nN1A4McoIINvM+oSvz+DQBC7PAHkEdWVQeK0SyV2xmF8E7gTaAssInnVxJ4fbU4AHgKfNzNz9lwSt\npTeEdeeGMu67LN8Fvk/wfscDt5Y+oJJl7wW+R1BHLwSuN7NxFV3c3Y8vrO8E7+0ygnoH5dRFd/9L\n+PMD4bljyij6l8ApBPXoeIJ1Kot3Iz2KYCKczsD/AI+H9aAEC/5AMxU4P6wPpxHWbTP7NsEY0e8R\n1MWxwPbw1FUEyXMrgn97z1owY2xZ5b9BUE/bE/xb/aOZ9S19rIhIbVEiKCJS0gwzyyKYlORc4EGA\nMHm4Fvipu+9w9z0Ei2xfEZ6XS9CdtJu757r7+179Qdjz3H2Guxe4e467f+buH7t7Xrjw+BMEiUd5\n/uPun4Tjs/5J8OG4TO4+zd33uPsBgg+5x4ctWjEESdfN7r4xnAX0o/C47wJvuvu/wnve7u5VSQTv\nC59lThjDs2EZee7+OyAB6B0e+wPgV+6+zANfhMd+QvBeFbbeXQG86+5by7je+wRr4hVOIjOe4Blv\nIkiyEwgmh4lz97XuvqqcuN8DzjCzo8LXL4avexAkBl9YMMvpBcAt7r7X3bcBD3OovhR3AfCVu78c\nvldTgdJdYNe5+5Punk+Q/HYk+IPDkfqruy8Pn/0LHKZuHI67v+vui8M6uogg2T5cnSzBzE4HfguM\ndffdYZll1sVKFjkBuMfdt4UT4dxNMLtqodxwf667vwZkc6iOlVYA9DezJHff7O5fhdt/QJCMfhrW\nxZXuvi6M/d/uvil8Hs8TtHYPLaPsi4C17v7XsL4vIJgc6duVvE8RkWpTIigiUtI4d08mWDftBuC9\n8AN/O6Ap8JkFXQOzgP8Lt0OQMK4E5obdye6ogVhKTFJjZsda0P10iwXdRf+XoIWoPMWTiX0Ei7Z/\ng5nFmtmUsEvbbg61hKWEX4kELR2lpZazvbJK39+tYXe7XeHzbcWh+zvctf4GXBn+fCXwj7IOChPz\n6QQtixAksoWtTSuBWwgSj21mNt3MOpVzvfcIlrIYAaQB7xIkP2cA73uwSH03ghk1NxerL08QtP6U\n1olizyKMM73UMVuK7d8X/ljm+1lJlaobFTGzk83sHTPLMLNdwI84fJ0sfm4qQRJ6tYfLalRQFyuj\nE7Cu2Ot14bZC20tNXFPmvYct9ZcT3M9mC7pYHxfuLrcumtn3LJjttfA9719O7N2AkwuPC4+dQNBi\nKSJSJ5QIioiUIWz5epmgpeh0gun9c4B+7p4cfrUKu7YRtmD83N17EnQV+5kdGmNWUctgeftLb/8T\nsBTo5e4tCbqfVnUh9LJ8l6Ab6kiC5Kt7uN0I7ns/UNb4wg3lbIegy2DTYq/L+oBbdH8WLJNwO3AZ\n0DpMxndx6P4Od61ngYvN7HigDzCjnOMgaLEaH3ZrPZmgFSYIxv05D2Z07BbGdn85ZbxH0Kp4Zvjz\nB8AwSnYL3UCwwHtKsfrS0t37lVHeZqBoxtiw9bkqM8jW5vTfFZX9HDATSHX3VsCfqUSdtGCM6Qzg\nD+7+erFdh6uLlYlnE8H7V6hruK3K3H2Ou59L0Pq6FHgy3FVmXQzr1JMEf0BqG9bhLyn7eWwA3itW\nN5LD7q7XH0msIiJHQomgiEgZLHAxwfi6r8NWnieBhy1c+NyCRdBHhz9fZGbHhB/idxEkkAVhcVuB\nw63vtxVoW4nuby0Ixqdlh60TNfWhsQVB0rKdIHn738Id4X1PA35vwSQ2sWZ2ajgBxz+BkWZ2mQWT\ni7Q1s8IuhguBS82sqQUTi/xPJWLIAzKAJmY2iaCbZaGngN+YWa/wvRlowZISuHs6wfjCfwAvFXY1\nLUvYBS8zLG+Ou2cBmFlvMzs7vK/9BEl/QTllrAj3X0nwYX43wXv4LcJE0N03A3OB35lZSwsmDjra\nzMrqNvkqMMDMxlkwoctPqFrLUEX1qzoqKrsFsMPd95vZUIJErjKmAUu91JhZDlMXKxnPv4BfmVm7\ncOzlJII/FFSJmXUws4vDsXwHCLqQFtaHp4BbzeyksC4eEyaBzQgS1YywjO8TtAiWZTZwrJldZWZx\n4dcQOzTWVESk1ikRFBEpaZYFC2rvJphc5epiY4N+QdD98+Ow29qbHBpf1Ct8nQ3MA/7o7u+E++4j\n+HCaZWZlTcqxlOAD7OrwmPK6JN5K8EF7D0FS+nz1brXI3wm60G0kWLz941L7byVYEP5TYAdBS1mM\nu68nGN/283D7QoIJOiAYD3eQ4IP73yg5+UxZ5hB0tV0exrKfkl1Hf0/QjXAuwXvzNJBUbP/fCGYW\nLbNbaCnP8c0JZRIIJgPKJOg22Z5g8pbyvEfQzXBDsdfGoQlPIJhMJJ7gme4kGEv4jYlD3D2TYGzY\nAwQJUF9gPkECUhmPELRy7jSzqZU8p7IqKvvHwD1mtocg6XqhkuVeAVxiJWcOHU7FdfFpgnGcWWZW\nVsvvbwme3SKCOvt5uK2qYggmsdlEULfPIPzDi7v/m+B3w3ME/xZnAG3cfQnwO4J//1sJ6uOHZRXu\nwRjjUeFz2ERQ5+4nqIciInVCC8qLiEi9Z2YjCFp+unk9/4/Nggl60oEJxf6YICIiUqPUIigiIvWa\nmcUBNwNP1dck0MxGm1ly2DW1cOxn6dYwERGRGqNEUERE6q1wTFUWQZfLP0Q4nOo4lWAmykxgDMHs\nteWOdRQREakudQ0VERERERFpZNQiKCIiIiIi0sg0iXQANSUlJcW7d+8e6TBEREREREQi4rPPPst0\n93aVObbBJILdu3dn/vz5kQ5DREREREQkIsxsXWWPVddQERERERGRRkaJoIiIiIiISCOjRFBERERE\nRKSRaTBjBEVEREREJPJyc3NJT09n//79kQ6lwUpMTKRLly7ExcUdcRkRSQTN7DzgESAWeMrdp5Ta\n/zBwVviyKdDe3ZPrNkoREREREamq9PR0WrRoQffu3TGzSIfT4Lg727dvJz09nR49ehxxOXWeCJpZ\nLPA4cC6QDnxqZjPdfUnhMe7+02LH3wgMqus4RURERESk6vbv368ksBaZGW3btiUjI6Na5USiRXAo\nsNLdVwOY2XTgYmBJOcd/B/h1HcUmIiIi0qjNWLCRB+csY1NWDp2Sk7htdG/GDeoc6bCknlESWLtq\n4vlGIhHsDGwo9jodOLmsA82sG9ADeLsO4hIRERFp1GYs2MidLy8mJzcfgI1ZOdz58mIAJYMiDUy0\nzxp6BfCiu+eXtdPMrjWz+WY2v7pNoyIiIiKN3YNzlhUlgYVycvN5cM6yCEUkcmTWrl1L//79a6Xs\nd999l4suugiAmTNnMmXKlArOiE6RaBHcCKQWe90l3FaWK4CflFeQu/8F+AvA4MGDvaYCFBEREWmM\nNmXlVGm7SE2oz92Rx44dy9ixYyMdxhGJRIvgp0AvM+thZvEEyd7M0geZ2XFAa2BeHccnIiIi0ih1\nSk4qc3vb5vF1HIk0FoXdkTdm5eAc6o48Y0F57USVl5eXx4QJE+jTpw/jx49n37593HPPPQwZMoT+\n/ftz7bXX4h60JU2dOpW+ffsycOBArrjiCgD27t3LxIkTGTp0KIMGDeKVV175xjWeeeYZbrjhBgCu\nueYabrrpJk477TR69uzJiy++WHTcgw8+yJAhQxg4cCC//nV0TH9S5y2C7p5nZjcAcwiWj5jm7l+Z\n2T3AfHcvTAqvAKZ74bsjIiIiIrXqiqGp/G7u8hLbDMjMPsjv5i7jpnN6ERcb7SOLJJrcPesrlmza\nXe7+BeuzOJhfUGJbTm4+t7+4iH99sr7Mc/p2asmvx/Sr8NrLli3j6aefZtiwYUycOJE//vGP3HDD\nDUyaNAmAq666itmzZzNmzBimTJnCmjVrSEhIICsrC4B7772Xs88+m2nTppGVlcXQoUMZOXLkYa+5\nefNmPvjgA5YuXcrYsWMZP348c+fOZcWKFXzyySe4O2PHjiUtLY0RI0ZUeA+1KSLrCLr7a8BrpbZN\nKvV6cl3GJCIiItKY5eYX8PriLbRIiKV5Yhxbdu2nU3ISN519DPPX7eTRt1fywcpMpl4xiNQ2TSMd\nrjQQpZPAirZXRWpqKsOGDQPgyiuvZOrUqfTo0YMHHniAffv2sWPHDvr168eYMWMYOHAgEyZMYNy4\ncYwbNw6AuXPnMnPmTB566CEgWBZj/fqyk9NC48aNIyYmhr59+7J169aicubOncugQcGKeNnZ2axY\nsaJxJoIiIiIiEl2eeG8VSzbv5omrTmJ0v6NK7Lt8aFeGH9uOX/5nMRc88j6/vaQ/F59QP8ZwSWRV\n1HI3bMrbbCxjDGrn5CSev+7Ual279BILZsaPf/xj5s+fT2pqKpMnT2b//v0AvPrqq6SlpTFr1izu\nvfdeFi9ejLvz0ksv0bt37xLlFCZ4ZUlISCj6ubBjo7tz5513ct1111Xrfmqa2vZFREREGrkVW/cw\n9a2VXDiw4zeSwEJjj+/EazcN59ijWnDz9IX87PmFZB/Iq+NIpaG5bXRvkuJiS2xLiovlttG9yzmj\n8tavX8+8ecF0I8899xynn346ACkpKWRnZxeN4SsoKGDDhg2cddZZ3H///ezatYvs7GxGjx7No48+\nWpTQLViw4IjiGD16NNOmTSM7OxuAjRs3sm3btureXrWpRVBERESkEcsvcG5/aRFNE2KZPKYfLHoB\n3roHdqVDqy5wziQYeBkAqW2a8vy1p/Do2yt59O0VfLZ+J49cMYgTUpMjfBdSXxXODlobs4b27t2b\nxx9/nIkTJ9K3b1+uv/56du7cSf/+/TnqqKMYMmQIAPn5+Vx55ZXs2rULd+emm24iOTnnDHZGAAAg\nAElEQVSZu+66i1tuuYWBAwdSUFBAjx49mD17dpXjGDVqFF9//TWnnhq0cDZv3pxnn32W9u3bV/se\nq8MaylwsgwcP9vnz50c6DBEREZF6ZdoHa7hn9hIevvx4Lon9CGbdBLnFuurFJcGYqUXJYKFP1uzg\nlukL2LbnAD8bdSw/GnE0MTGGyNdff02fPn0iHUaDV9ZzNrPP3H1wZc5X11ARERGRRmp9ZjbPzvmQ\nn3TdwLjc12H2T0smgRC8fvPub5w7tEcbXr95BKP7HcUD/7eMK5/+L1t27a+jyEWkutQ1VERERKQh\nc4d922H7yhJfvn0VR21byduxB2EbpeZzL2V3Ojx3OfQaBceODrqMAq2axvHYdwcxYn4Kk2cu4fxH\n0rj/WwMZVc44QxGJHkoERURERBqCA3tg+6ow0VtVLOlbBQd2HTouJg7a9GBjTCdey+vB8cefxMmD\nh0LbY+Dpc2HXhm+WHd8cti2B5f8HrwIdBgQJ4bGjsc4ncfmQrgzu3oab/rWAa//xGVed0o1fXtiH\nxFKTgIhI9FAiKCIiIlJf5B2AnWtLte6FSV928SntDVqlQtujYeC3gySv7THB61Zd2ZKdx/m/f49+\nXVvyg2+dAoVj+86ZVPYYwYsehgHfhoxlQTK4Yi588DC8/xA0bQu9/j979x3Wddk9cPx9s0GWCi7c\nCxe4M7dprtTc5taGZdrul9l4fHx8KisbT2nLMi1H7pyZmlmO3Av33nsggmy4f3/coKiAwncxzuu6\nvhfy4TMOJsThPvc5bahQqQ3zn3qET/4+z/drj7Pp+FW+7FObKsV87fk3JIR4QJIICiGEEELYUyZd\nOQFITjKrcleP3rWyd8Qc12kGbRcINAlepdZQqMLthK9QOZPApUNrzbsLdpCQnMyH3ULvbPCSGkdG\n8RWpYl5NXoHoa3D0Tzi03CSHu37B3cmFd0o3pHfDJrwRVoLHJ0TzzmNVGdiwzD0z3YQQjiVdQ4UQ\nQggh7CVs9r0rbs6uUL6leXv1CFw7Bknxtz/u5mNW8tKu6hWuYBI/z6yPbVi06xwv/bKDdx6rypBm\n5a3wSQFJiXBmCxxebhLDS/sAuOBSgqWxNQkv+QhP9e1HIV9v6zxP5GjSNdQ+LO0aKomgEEIIIYQ1\naA3xURB1KeV1EW5eNm+jLkLUZTj6ByQlpH99YJU0iV7F2yt83kXASqtpV6PiaP35GkoV8mL+841w\nttW4h+un4NBy9KHlJB37G5fkeKLwJLZUcwLqPG5WML0dO0NN2E5OSARPnDhBx44d2bNnzwOdP2XK\nFNq0aUOJEiUyPWfr1q1MmDDBWmFaxNJEUEpDhRBCCJG33K/0MqsSYm4ndzdTEry0yd6t45cgIfre\n65WTKeH0LpJxEoiC4ZuyH+MD+s/ifUTGJjCuR6jtkkAA/9Lw0BDUQ0Nwib/Jqa3LCFs9m3qnNsHp\n39EoVFAdqNzONJ0pFmq1ZFfkQtb+ms2GKVOmUKNGjUwTQVtJTEzExcX+aZkkgkIIIYTIO+4uvYw4\nbd6HO3+wTIy/vVp3a9UuvZW8SxB3I/1neRWGAkVMglfyIfPWuwh4F01J/Iqal1chcErpnvl5jfS7\ncqaMY7ClP/ZdZNGuc7z6aGUqF/Wx+fNucStA6UY9CKzXlTGL9xK2dS19Cu6nR+JePFZ/AKvfB5/i\nKaMp2kH55uBWwH7xCcd60K/ZbEhMTKRfv35s376d6tWr8/PPP/PJJ5+wePFiYmJiaNSoEd999x3z\n5s1j69at9OvXD09PTzZs2MCePXt4+eWXuXnzJu7u7qxatQqAc+fO0a5dO44ePUrXrl35+OOPAfD2\n9ubll19myZIleHp6snDhQooWLcqJEyd46qmnuHLlCoGBgUyePJnSpUszePBgPDw82LFjB40bN8bX\n15fjx49z7NgxTp06xeeff87GjRtZtmwZQUFBLF68GFdXV4v+Pu4mpaFCCCGEyDsySrRcvaBkvduJ\nXkx4+te7+91O5rxTk7kiKQlf0dvJXoFAs6cvq9LbI+jqCZ2+tOkKSERMAm0+/5uCXm4seqEJbi5O\nNnvW/fy+5zxvzttNQlIyH7UrTkfPPajDy+HInxAfCc7uUK6ZWSms1AYKlnFYrCJ77ihZXDYSLuzO\n+OQzWyAp7t7jzu5Qsn761xQLgfYfZhrDiRMnKFeuHOvWraNx48Y89dRTVKtWjaeeeopChQoBMGDA\nAHr16kWnTp1o0aIFn3zyCfXq1SM+Pp4qVaowa9Ys6tevz40bN/Dy8mLatGmMGTOGHTt24O7uTnBw\nMOvWraNUqVIopVi0aBGdOnVixIgR+Pr68u6779KpUyd69OjBoEGD+PHHH1m0aBELFixg8ODBXLly\nhYULF+Ls7Mzo0aP5448/WL16Nfv27aNhw4bMmzeP9u3b07VrVwYNGkSXLl0y/ntOIaWhQgghhMif\nIs6kfzwh2oxeCKgEZZvcXsm7I7krAq4eto3vfl05bWTsb/u5HBnHxAH1HJoEArSrUZzQkv68Omsn\nLy46w4qa1Xi/ay98XTSc+gcOrYBDy+C3/zMXFKl2e7WwZH3YO9/hZYTCitJLAjM7ngWlSpWicePG\nAPTv358vv/yScuXK8fHHHxMdHc21a9eoXr06nTp1uuO6gwcPUrx4cerXN4mor+/tESitWrXCz88P\ngGrVqnHy5ElKlSqFm5sbHTt2BKBu3bqsXLkSgA0bNjB//nzAJJ4jRoy4da+ePXvi7Hx71mb79u1x\ndXUlJCSEpKQk2rVrB0BISAgnTpyw+O/jbpIICiGEECLv8C5y1zy9FH6l4OkV9o8nPaG97Jq4rD9y\nhZlbTvNcs/LULJX1LqO2UMLfkxlDHuabv47w+R+H2XEqnC9616Zu+RZQvgW0+wCuHEnpQvo7bJgA\n6/9nVnYT40AnmRtZsYxQ2Mh9Vu4yLpcuBU8utejRd48sUUoxbNgwtm7dSqlSpRg9ejSxsbFZuqe7\nu/utPzs7O5OYmAiAq6vrreelPZ6ZAgXuLIFOvbeTk9Md93Nycnqg+2WVY38lJIQQQghhLRf2QFzU\nvcddPc2qUT4UHZ/IyPlhlAsowKutKzs6nDs4OyleaFmJ2c81BKDXdxv4ctVhkpJTti0FVISGw2HQ\nYhhxDHpOMcdTk8BUCTFmhVDkTq1G3Tvz0kpfs6dOnWLDhg0AzJgxgyZNmgAQEBBAVFQUc+fOvXWu\nj48PkZGRAAQHB3P+/Hm2bNkCQGRkZLYTsUaNGjFz5kwApk+fTtOmTbP9+VibJIJCCCGEyP3Oh8FP\nncxcvTbvmdUElHlr4/13Odknyw9x+loMH3YLwcPV+f4XOEDdMgX57eWmdAwtzmcrD9Fn4kbOXo+5\n8yQPP6je9c69lWllVBIscr7QXuZr1AZfs8HBwXz11VdUrVqV8PBwnn/+eYYMGUKNGjVo27btrdJP\ngMGDBzN06FBq1apFUlISs2bN4sUXX6RmzZq0bt06yyuHqcaPH8/kyZMJDQ1l6tSpfPHFFxZ/XtYi\nzWKEEEIIkbud2wlTu4BrARi8GApZaUh6LrftZDg9vv2H/g3K8N8uNRwdzn1prZm//SyjFu7B2Unx\nYfdQHgspfudJGZURunjCG0fAXQbW5wQ5YY5gfmBpsxhZERRCCCFE7nVuB/z8OLh5w+AlkgSmiEtM\n4s15YZTw8+TN9lUcHc4DUUrRvW5Jlr7UlHIBBRg2fTsj54URHZ+mJC+9MkInV0iMgUmt4dox+wYt\nRC4miaAQQgghcqez2+CnzqZscPBSKFTO0RHlGONXHeHIpSje71oDb/fc1RuwbEAB5j7fiOdbVGDW\n1tN0HL+OPWcjzAfTKyPs8jUM+BUiz8PER+DIKofGL0RuIYmgEEIIIXKfM1vh5y5mT+DgpTJrLo29\n5yL49u+jdKsTRIvgIo4OJ1tcnZ14s10Vpj/dgJtxiXT7+h9+WHuM5GRtksFX98Do6+ZtaC+o0BKG\nrAbfIJjeA9Z/AXlk+1NulVe2n+VU1vj7lT2CQgghhMhdTm+Gqd2gQIApB/Ur6eiIcozEpGS6fL2e\nCxFx/PFaM/y93BwdksWu3YxnxNww/th/keaVA2lVtQjf/X2Mc9djKOHvyRttg+lSO8icHH8TFgyD\nfQugRnd4fAK4eTn2E8iHjh8/jo+PD4ULF75nhIOwnNaaq1evEhkZSblyd1ZCZGWPoCSCQgghhMg9\nTm2Ead3NIPhBi8EvyNER5Shf/3WEj38/yDf96tD+7kYruZjWmmmbTjF64R6S7vrR1dPVmbHdQm4n\ng1rDus/NSIliNeCJ6bJibGcJCQmcOXMm2502xf15eHhQsmRJXF1d7zguiaAQQggh8p6TG0zZn08x\nkwT6lnB0RDnK0ctRtP9iLa2qFOGb/nUdHY5NPPT+H1yKjLvneJC/J+tHtrzz4OGVMPdpcHI2MwjL\nN7dPkEI4kHQNFUIIIUTecmK9WQn0KW72BEoSeIfkZM2bc8PwdHXmP52rOzocm7mcThIIcO7uuYMA\nlVrDs6uhQCBM7QobvpZ9g0KkIYmgEEIIIXK242vNSqBfSZME+hRzdEQ5zs8bTrD1ZDijOlajiI+H\no8OxmRL+nukeL+Lrnv4FhSvAkFUQ3B6WvwULns94KL0Q+YwkgkIIIYTIuY79BdN7gn8Z0xjGp6ij\nI8pxTl+L5uPlB2leOZBudfL2nsk32gbj6ep8z/HImAQ2H7+W/kXuPtBrKrR4G3b9Aj+2g4gzNo5U\niJzPIYmgUqqdUuqgUuqIUmpkBuf0UkrtU0rtVUrNsHeMQgghhHCwo3/CjCfMkPhBi8E7d45CsCWt\nNW//uhsFfNAtJM93aOxSO4ix3UII8vdEYfYGvvNYVYr5edLvh43M2Xo6/QudnKDFm9D7F7h6FL5r\nbsqNhcjH7N4sRinlDBwCWgNngC1AH631vjTnVAJmAy211uFKqSJa60uZ3VeaxQghhBB5yJE/4Je+\nEFAJBi6CAoUdHVGONHvraUbMDeO/naszoGFZR4fjMBHRCQybsY31R64ytHkFRrQNxskpg6T48iGY\n2QfCT0C7D6H+M5DHE2iRf+T0ZjEPAUe01se01vHATKDzXecMAb7SWocD3C8JFEIIIUQecnilSQID\nK5uVQEkC03XpRizvLdnHQ+UK0a9B/h6P4OflypQnH6Jvg9J8+/dRhk7bRnR8YvonB1aGIX9ChVbw\n2//BohcgMf0mNELkZY5IBIOAtOv2Z1KOpVUZqKyUWq+U2qiUame36IQQQgjhOIeWw8y+EBhsVgK9\nCjk6ohxJa827C/YQl5jMR91DM179ykdcnZ14v0sNRnWsxh/7L9Ljmw2cj8igMYyHH/SZCc3egB3T\nYPJjcOO8fQMWwsFyarMYF6AS0ALoA3yvlPK/+ySl1LNKqa1Kqa2XL1+2c4hCCCGEsKqDy2BmPyhS\nDQZJEpiZpbvPs2LfRV5rXZlyAQUcHU6OoZTiqSblmDS4PqeuRdN5wnp2nb6e/slOTtDyXej1M1za\nDxObw6lN9g1YCAdyRCJ4FiiV5v2SKcfSOgMs0lonaK2PY/YUVrr7RlrriVrrelrreoGBgTYLWAgh\nhBA2dmApzBoAxUJg4ELwLOjoiHKs8Jvx/HvhXkKC/Hi6STlHh5MjPRJchHnPN8LNxYle321gaVgm\nq33VOsMzf4CrF0zpANum2C1OIRzJEYngFqCSUqqcUsoN6A0suuucBZjVQJRSAZhS0WP2DFIIIYQQ\ndrJvEcweCMVrwsAF4HlPEZBIY8ySfUTEJPBxj1BcnHNqcZfjBRfzYcHwxtQI8mP4jO2MX3WYDJsk\nFq1mhs+XawaLX4Ylr0JivH0DFsLO7P7dQ2udCLwALAf2A7O11nuVUmOUUo+nnLYcuKqU2gesBt7Q\nWl+1d6xCCCGEsLG9C2DOYChRBwb8avZuiQytPnCJX3ecZdgjFala3NfR4eR4Ad7uTH+mAV1rB/Hp\nykO8OmsnsQlJ6Z/sWRD6zYHGr8DWH+GnThB50b4BC2FHdh8fYSsyPkIIIYTIZfbMg3lDoGR96D/X\nDP4WGYqMTaDN52vw8XBhyYtNcXOR1cAHpbXm67+OMm75QeqU9ue7AfUI9HHP+ILdc2HhCyY5fGIa\nlKxrv2CFsEBOHx8hhBBCiPxu91yY9wyUaiBJ4AMau+wAF2/E8nGPmpIEZpFSiuGPVOTrfnXYd/4G\nXb5az4ELNzK+IKQHPLMSnF1gcnvYMd1+wQphJ/JdRAghhBD2FTYb5g+B0g1NKZ4kgfe14ehVZmw6\nxdNNylGrlOyhzK7HQooz+7mGJCQl0/3rf/jzQCaln8VCYMhfULoBLBwGv42ApAS7xSqErUkiKIQQ\nQgj72TUTfn0OyjROSQK9HR1RjhcTn8TI+WGUKezFa62DHR1Orhda0p+FLzSmbEABnvlpKz+sPZZx\nE5kChaH/r/DwcNj8HfzcBW5esW/AQtiIJIJCCCGEsI8d0+HXoVC2KfSdDW4y/+5BfLbyICevRvNh\nt1A83ZwdHU6eUNzPkzlDG9K6WlHeW7qft3/dQ0JScvonO7tAuw+g63dwditMbAHndto1XiFsQRJB\nIYQQQtje9qmwcDiUbwF9Z4Gbl6MjyhV2nr7OpHXH6dugNA0rFHZ0OHmKl5sL3/Sry7AWFfhl8ykG\n/biZiOhMSj9r9oanfget4ce2psRZiFxMEkEhhBBC2Na2KbDoBajQEvr8Aq6ejo4oV4hLTGLE3F0U\n9fXgrfZVHB1OnuTkpBjRrgqf9qzJlhPX6Pr1eo5fuZnxBSVqw7N/QVBds891+TuQlGivcIWwKkkE\nhRBCCGE7WyaZAd0VW0PvGZIEZsFXq49y6GIUH3QNwcfD1dHh5Gnd65ZkxpCHuR6TQJev1vPP0Uz2\nAXoHwsCF8NCzsGECTO8O0dfsF6wQViKJoBBCCCFsY/P3sPQ1qNQWek8HVw9HR5Rr7D9/g69XH6Fr\n7SAeqVLE0eHkC/XLFmLBsMYU8XFn4KTNzNx8KuOTnV3hsXHw+AQ4+Y/ZN3hht91iFcIaJBEUQggh\nhPVt+g5++z+o3B6emAoumQzvFndITEpmxNww/DxdGdWxmqPDyVdKF/Zi3rBGNKoYwMj5u3lvyT6S\nkjPoKApQZwA8uQyS4mFSG9gz337BCmEhSQSFEEIIYV0bv4FlI6BKR+j1sySBWTRp3XF2n43gP52r\nU7CAm6PDyXd8PVz5cVA9Bjcqyw/rjvPsz1uJistkH2DJembfYLEQmPskzHgCPq8Oo/3h8xrSVEbk\nWJIICiGEEMJ6/pkAv4+Eqp2g5xRwkUQmK45djuKzlYdoU60oHUKKOzqcfMvF2YnRj1fnv52r89eh\ny/T45h/OhEdnfIFPMRi0xIxGOfQ7RJwBNESchsUvSTIociRJBIUQQghhHeu/gBXvQLUu0GOy2Ucl\nHlhysmbkvN24uzjxXpcaKKUcHVK+N6BhWaY8WZ+z12Po8tV6tp8Kz/hkFzcIP3Hv8YQYWDXGZjEK\nkV2SCAohhBDCcms/g5WjoHo36D5JksBsmL7pJJtPXOPdjtUo4iuNdXKKppUC+XVYIwq4u9B74kYW\n7jyb8ckRZzI4fho2TYSbmXQjFcLOLEoElVIy2VQIIYTI79aMg1X/gRo9oNv34Ozi6IhynTPh0Xy4\n7ABNKwXQs25JR4cj7lKxiA8LhjWmVil/Xp65k89WHiI5vSYyfhn8t3NyhWVvwKfBZg/hnnlmpVAI\nB7L0O/VGpdROYDKwTGudSVslIYQQQuQJYbNNqVvEGXD3gbgbENILunwjSWA2aK15+9c9aOCDriFS\nEppDFSzgxrSnG/DOr7v5ctVhjl6O4tOeNfFwdb59UqtRZk9g2iTP1RM6fQlFqsHu2RA2x+wjdPOB\nap0htBeUbQJOzvc+VAgbsvS7dWXgUeAp4Eul1Gxgitb6kMWRCSGEECLnCZt95w+6cTdAOUPFVpIE\nZtP87WdZc+gyoztVo1QhL0eHIzLh5uLExz1CqVjEmw9/P8CZ8Bi+H1D3dilvaC/zNvUXJX4lTXKY\nerxYDWj1bzixznwt7VsIO6eBTwkI7QmhT0DR6o755ES+o6y1iKeUegSYBhQAdgEjtdYbrHLzB1Cv\nXj29detWez1OCCGEyJ8+qwY30tkj5VcKXt1j/3hyuUuRsbT+bA2Vingz+7mGODnJamBusWLvBV6e\nuRN/L1d+GFSP6iX8sn6T+Gg4tMwkhUf+gOREKFrDJIQhPcC3hPUDF3maUmqb1rreA51rSSKYskew\nPzAAuAhMAhYBtYA5Wuty2b55FkkiKIQQQtiA1nD5ABxeCUdWwvE1GZyoYPR1u4aWFzw/bRurDlxi\n2ctNqRDo7ehwRBbtPRfBMz9tJSImgf89UYs21Ytl/2Y3r5iB9GGz4OxWQEG5ZlCztxnH4u5jtbhF\n3mXPRPAQMBWYrLU+c9fH3tRaf5Ttm2eRJIJCCCGElcRFwfG/U5K/P0zHQ4Ai1SHiFMRF3nuNrAg+\nsAU7zjJu+UHOXjfltR1DizGhb10HRyWy69KNWIb8vJWwsxGMbFeFZ5uVt3yf59WjZpUwbBaEHwcX\nT6jymFkprNBSuvKKDNkzEVQ5pUGMJIJCCCFENmkNlw+aFb/DK+HUBkiKN80syjeHSq2hYmvwC7p3\njyDcboaRug9KZGjBjrO8NX83MQlJt455uDrxYbdQutQOcmBkwhKxCUm8PmcXS8PO81DZgpwJj+F8\nRCwl/D15o21w9v/bag1ntpiEcM88iAkHrwCo0d0khUF1QJoLiTTsmQiuBHpqra+nvF8QmKm1bpvt\nm2aTJIJCCCFEFsTfNGWeh1fA4T/MSh9AYFWT+FVqDaUeNkOy75a2a+jdzTBEphp/+OetlcC0gvw9\nWT+ypQMiEtaSnKx5fto2lu+7eMdxT1dnxnYLsTzRT4w3K/Rhs+DgMkiKg8IVU/YT9oRCdtuRJXKw\nrCSClrb3CkxNAgG01uFKqSIW3lMIIYQQ1qY1XDmcsuq3Ak7+k7Lq5w3lmkPT16Dio+Bf6v73Cu0l\niV82nUsnCczsuMg9nJwUe87duOd4TEIS45YftDwRdHEz5aFVHoPYCNNxNGw2rH7fvEo1MElh9a7g\nVciyZ4l8wdJEMEkpVVprfQpAKVUGyBGlokIIIUS+F38Tjq+9nfxdT131qwIPPQuV2kDphumv+gmb\nKOrrzoUbcfccL+Hv6YBohLXZLdH38IM6A83r+mnYMxd2zYKlr8GyN83XdmgvqNwOXD2s+2yRZ1ia\nCL4DrFNK/Q0ooCnwrMVRCSGEECLrtIarR253+Dyx3pSPuXpB+RbQ+BVT8ulf2tGR5kvJyRofD5d7\nEkFPV2feaBvsoKiENZXw90y39FcD41cdZkiz8ncOoLcG/1LQ5FXz9X1htykd3T0XDi4Fdz+o3tms\nFJZuBE5OUtotbrF4jqBSKgB4OOXdjVrrKxZHlQ2yR1AIIUS+FB9thlMfXmGSv/AT5nhAZbMqUPFR\nKNMIXNwdGqaAH9Ye472l+3mifknWHb7KuesxljcTETlKes2A3F2cqFrMh51nIihb2IvRj1enRbCN\nd1IlJ5nOv2GzYd8iSLhpOvsWrQHHVkNi7O1zpdlTnmK3ZjEpDysIVAJurTtrrTMaMmQzkggKIYTI\nN64eTWnystIkgamrfuWamcSvUmsoWNbRUYo09p+/QecJ62kRHMh3A+paPl5A5Fip40HuTvTXHr7M\nvxfu5diVm7StXpR/daxGyYJetg8o/qZpLrNrpvllUXpk/EueYc+uoc8ALwMlgZ2YlcENWmu7t72S\nRFAIIUSecXfpVouR4F3UJH6HV5i5YgCFK93u8Fm6kewFyqFiE5LoPGE916LjWf5KMwoVkD2Z+VVc\nYhKT1h1n/KojaDQvPFKRIc3K4+5i5XLRjIz2J/12HgpGX0/nuMht7Nk19GWgPqYk9BGlVBXgAwvv\nKYQQQuRPifGwfSqsePt26VbEaVg43PzZxdOs+jUcblb+pF18rjBu+UEOXoxk8pP1JQnM59xdnBnW\noiKdawXx/tJ9fLLiEPO2n2X049VpXjnQ9gH4lTTfU+7mK6XJ+ZGliWCs1jpWKYVSyl1rfUApdd/d\nzkqpdsAXgDPwg9b6w7s+PhgYB5xNOTRBa/2DhbEKIYQQhq2aJWgN8VEQc920d49NeZvu++l8LCE6\n43sXCIBX9pj9PCLXWH/kCpPWHWdgwzI8Yut9YSLXCPL35Ot+dVlz6DKjF+1l0I+baVe9GP/qVI0g\nW3aQbTUKFr8ECXc1tHFxh+hrMnYin7G0NPRX4EngFaAlEA64aq0fy+QaZ+AQ0Bo4A2wB+mit96U5\nZzBQT2v9woPGIqWhQgghHkjY7Ht/EErbLCEpISUxS5uwpZO0ZZTs6aSMnw2mi5+nn2n/7uFv3nr6\np/zZH1a/l8GFUrqV21yPjqfd/9ZSwN2ZJS82xdPNTuV/IleJS0zih7XHGf/nYQBebFmJZ5qWs125\n6N2/CKvSAbb+CIUqwID54FvCNs8VdmHXZjFpHtoc8AN+11rHZ3JeQ2C01rptyvtvAWitx6Y5ZzCS\nCAohhLCFz2ukXxqlnE1CGB+V+fXObukkcKl/vju5u+t9d19wus8PdxnFJ80cchWtNS/M2MHyvRdY\nMLwxNYL8HB2SyOHOXo/hvSX7WLbnAuUCCtivXBTg+Br4pa/5PjXgVwioZJ/nCquzyx7BlJW9vVrr\nKgBa678f8NIgIO3/4c4ADdI5r7tSqhlm9fBVrXU6/1cUQgghsijiTPrHdRLUGXRnApdeMufiAbbs\n+Jhe6Zarpzkuco1fd5xl6e7zjGgXLEmgeCBB/p58078uf6cpF21foxjvdrRxuSiYvceDl8C07vBj\nW+g3F4Lq2PaZwuGynQhqrZOUUgeVUqW11qesGRSwGPhFax2nlHoO+AlTenoHpdSzpAywL11ahuMK\nIYS4j2N/mSQuvWoYv1LQLgf0O0vdqygDn3Ot09eiGbVwLw+VLcRzzSo4OhyRyyf6vogAACAASURB\nVDSvHMjvrzS9VS7618HLvNCyom3LRQFK1IKnV8DULvBTJ+g9Hcq3sN3zhMNZukdwDVAb2AzcTD2u\ntX48k2vuWxp61/nOwDWtdaa/TpPSUCGEEBlKToI1n8BfY80YhtjrMlBZ2ERSsqb3xA0cOB/Jby83\npVQhO8yJE3nWmfBo3luyn9/3XqB8SrloM1uXi944D9O6wdUj0G0iVO9q2+cJq7Ln+Ih/ZeOaLUAl\npVQ5TFfQ3kDftCcopYprrc+nvPs4sN+iKIUQQuRfUZdh/hA4thpCn4COn8OBpbLiJmziuzVH2XIi\nnM961ZQkUFisZEEvvh1Ql78OXmL0or0M/HEzj4UU490O1Shhq3JR3+Lw5G8wozfMeRKir0L9Z2zz\nLOFQVmsWk6WHKvUY8D/M+IgftdbvK6XGAFu11ouUUmMxCWAicA14Xmt9ILN7yoqgEEKIe5z8B+Y+\nZdqiPzYO6gy07f4+ka/tORtBl6/W07ZGMSb0qY2Sf2vCiuISk/h+zTEmrD6CQvFiq4o806Q8bi5O\ntnlgfDTMfRIO/Q4t3obmI+T7Zy5gt66hSqlIIPUGboArcFNr7Zvtm2aTJIJCCCFuSU6Gf740q34F\ny0DPn6B4qKOjEnlYTHwSHcev5WZcEr+/0hR/LxkcL2zjTHg0/12yj+V7L1I+sABjHq9Bk0oBtnlY\nUgIsehF2/QIPPQvtPgInGyWewirsVhqqtfZJ81AFdAYetuSeQgghhEWir8GC581vsat1gcfHg4fd\nfz8p8pmxy/Zz9PJNpj/TQJJAYVMlC3rx3YB6rE4pF+0/aRMdQorzbseqFPezcrmosyt0/hq8CsOG\nCaZMtMu34CL/xvMCq6X02lgAtLXWPYUQQogsObMNvmsOR1ZB+3HQc4okgcLmVh+4xM8bTvJMk3I0\nrmijlRkh7vJIcBGWv9KM11tXZtWBi7T69G+++eso8YnJ1n2QkxO0fR9aj4E98+CXJyDuPvNWRa5g\naWlotzTvOgH1gOZa64aWBpZVUhoqhBD5mNaw6TtY8S74FDcJYMm6jo5K5ANXo+Jo+7+1BHi7sWB4\nYzxcbdjeX4gMnL5mykVX7LNxueiOaaZUtERt6DsHChS2+iMW7DjLuOUHOXc9hhL+nrzRNpgutYOs\n/py8KiuloZauCHZK82oLRGLKQ4UQQgj7iI2A2QPh9zeh4qPw3N+SBAq70Fozcv5ubsQk8L/etSQJ\nFA5TqpAXEwfWY/Lg+iQla/pP2sTwGds5HxFj3QfV7g9PTIeLe2FyO7h+2qq3X7DjLG/N383Z6zFo\n4Oz1GN6av5sFO85a9TnCcEjXUFuQFUEhhMiHzofBnEEQfhIeHQ2NXpSudsJuZm05xZvzdvNuh6o8\n07S8o8MRAoDYhCQmrjnGV6uP4OykeKlVJZ5qXM663UVPrIdfeoO7Dwz4FQKDs32rhKRkrt2M53Jk\nHIN+3MzVm/H3nBPk78n6kS0tiTjfsGfX0J+Al7XW11PeLwh8qrV+Kts3zSZJBIUQIh/RGrb/BL+N\nME0Mek6G0tKrTNjPiSs3eezLtdQq5c+0pxvg5CS/gBA5y+lr0YxZso+V+y5SIbAAYzrXsO4e1gu7\nYWo3SE6AfnOh5O3cIylZEx5tkrsrUXFcjoy7889RcVyJjOdyVBzX0kn87qaA4x92sF7seZg9B8qH\npiaBAFrrcKVUbQvvKYQQQmQsLgqWvgZhs6BCS+j2PRSQBh3CfhKTknll1k5cnBSf9qopSaDIkUoV\n8uL7gfX488BFRi/aR78fNtExtDj1yhbk+zXHs7wHT2tNREwCV6LiuBQZx+XIQsTV/JHW24biNakD\nXwaMYnVi6K3kLin53sUmD1cnAn3cCfR2p2yAF/XKFjTv+7gT4O3Ou7/u4XJU3D3XlfC3cjdUAVie\nCDoppQpqrcMBlFKFrHBPIYQQIn2X9sPsQXD1MDzyDjR9HZxkX5awrwmrj7Dz9HUm9K1t/Xb9QlhZ\nyypFaVQhgO/+PsaXqw6xJOz8rY+dvR7DyPlhXI6Mo2Yp/0xW78zbhKR7k7vizu/wk9vHvHJ5FL4B\nIzhepf2txC5tkhfo404BN2dUJuX7MfFJvDV/NzEJSXccH9iojPX+QsQtliZtnwIblFJzUt7vCbxv\n4T2FEEKIe+2aCUteBTdvGLAAyjd3dEQiH9p+Kpzxfx6hW+0gOoaWcHQ4QjwQD1dnXn60EjM2n+Ti\njTtX3GITknn/t/13HHNSEOB9O4GrXNTnrsTOjSI+7gR6e+Dr6YKK6wC/9OW5k2Ohvj80eC5bcaau\nTKZ2DS3i605UbCJzt56hf4MyFHCX9SZrsrhZjFKqGpC6e/NPrfU+i6PKBtkjKIQQeVRCDCwbAdt/\nhjJNoMck8Cnm6KhEPnQzLpHHvlxLYpJm2StN8fVwdXRIQmRJuZFLyegn/6lPP3Rr9a6glxvOWS15\nToiFeU/DgSXQbAQ88rZVmnetO3yFgT9uokNoCb7sXSvTFUVhx/ERSqmHgdNa6wla6wnAGaVUA0vu\nKYQQQtxy5Qj88KhJApu+DgMXShIoHOa/S/Zx6lo0nz9RS5JAkStltNcuyN+TppUCqVLMlwBv96wn\ngQCuHtDzJ6g9ANZ8bCo4kpPuf919NKkUwOttglm86xxT/jlh8f3EbZb2kf0GiErzflTKMSGEEMIy\ne+bDxBZw46zpSNdqFDhLWZBwjBV7LzBzy2mGNq/AQ+UKOTocIbLljbbBeN4179LT1Zk32mZ//MMd\nnF3g8fHQ5DXYNhnmPgmJ9zZ/yarnm1fg0apFeH/pfraeuGaFQAVYnggqnaa2VGudjDSLEUIIYYnE\nOPjtDfMDRJGqMHQdVGrt6KhEPnYpMpaR83dTvYQvrz5a2dHhCJFtXWoHMbZbCEH+nijMSuDYbiEP\n1DX0gSkFj/4b2n4A+xbC9B4QF2nRLZ2cFJ/2qkVQQU+GTd/OpchYKwWbv1k6R3A+8Be3VwGHAY9o\nrbtYHlrWyB5BIYTIA8JPwJzBcG4HNHzBDIl3lhI84Thaa56csoUNR6+y9KUmVCzi4+iQhMg9ds2E\nBcOgWIip7PAOtOh2+8/foOvX66lZ0p/pzzTAxdnSNa28x257BIGhQCPgLHAGaAA8a+E9hRBC5EcH\nfoPvmsHVY/DENGj7viSBwuGmbTzJXwcv806HqpIECpFVNXtDn1/g8kH4sS2En7TodlWL+/JB1xA2\nHb/Gx8sPWinI/MuiRFBrfUlr3VtrXURrXVRr3VdrfclawQkhhMhA2Gz4vAaM9jdvw2Y7OqLsS0qA\nFe/CzD5QsCw89zdU7eToqITgyKVI3lu6n+aVAxnwsMwxEyJbKrc1jb6ir8CkNnDRsgED3eqUZMDD\nZZi45hjLdp+//wUiQ5Z2DfVQSg1XSn2tlPox9WWt4IQQQqQjbDYsfgkiTgPavF38Uu5MBiPOwpQO\n8M94qP8MPLUCCpVzdFRCEJ+YzCuzdlLA3YVxPUOlZb0QlijdAJ783ewfnNwOTm206HbvdqxKrVL+\n/N+cXRy5FHX/C0S6LC0NnQoUA9oCfwMlAct2gwohhMjcqjFmtl5aCTGw7E04txPiox0TV1Yd+QO+\nawoX90L3SdDhU9N+XIgc4ItVh9hz9gZju4VQxEf+XQphsaLV4Knl4BUAP3eBQ8uzfSt3F2e+7lcH\nd1dnhk7bxs24RCsGm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9M2nqRkQU+aVQ50dChCiJxKKWj5L2j8MmydBMveNL/oz0Dloj58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\nTq5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UH3X3em43mUf+kpvfW8TzTy/hoR81p3OL/UzsksLmrd7C2CensaqgkNvG9OX8oR2D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\nEBQREREREUkz/w8Th/0rSpHZtgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faf29fc9d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot results of weight scale experiment\n",
    "best_train_accs, bn_best_train_accs = [], []\n",
    "best_val_accs, bn_best_val_accs = [], []\n",
    "final_train_loss, bn_final_train_loss = [], []\n",
    "\n",
    "for ws in weight_scales:\n",
    "  best_train_accs.append(max(solvers_ws[ws].train_acc_history))\n",
    "  bn_best_train_accs.append(max(bn_solvers_ws[ws].train_acc_history))\n",
    "  \n",
    "  best_val_accs.append(max(solvers_ws[ws].val_acc_history))\n",
    "  bn_best_val_accs.append(max(bn_solvers_ws[ws].val_acc_history))\n",
    "  \n",
    "  final_train_loss.append(np.mean(solvers_ws[ws].loss_history[-100:]))\n",
    "  bn_final_train_loss.append(np.mean(bn_solvers_ws[ws].loss_history[-100:]))\n",
    "  \n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Best val accuracy vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Best val accuracy')\n",
    "plt.semilogx(weight_scales, best_val_accs, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_best_val_accs, '-o', label='batchnorm')\n",
    "plt.legend(ncol=2, loc='lower right')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Best train accuracy vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Best training accuracy')\n",
    "plt.semilogx(weight_scales, best_train_accs, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_best_train_accs, '-o', label='batchnorm')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Final training loss vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Final training loss')\n",
    "plt.semilogx(weight_scales, final_train_loss, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_final_train_loss, '-o', label='batchnorm')\n",
    "plt.legend()\n",
    "plt.gca().set_ylim(1.0, 3.5)\n",
    "\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inline Question 1:\n",
    "Describe the results of this experiment. How does the scale of weight initialization affect models with/without batch normalization differently, and why?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Answer:\n",
    "\n",
    "when the weight initialization is in [10e-4, 10e-2], \"batchnorm\" is better than \"non-batchnorm\". And in most cases, the accuracy of \"batchnorm\" is better than that of \"non-batchnorm\"\n",
    "\n",
    "THe scale of weight initialization has much effect on models without batch normalization. I think the reason of why it is is that batch normalization shape data into a normal stauts."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batch normalization and batch size\n",
    "We will now run a small experiment to study the interaction of batch normalization and batch size.\n",
    "\n",
    "The first cell will train 6-layer networks both with and without batch normalization using different batch sizes. The second layer will plot training accuracy and validation set accuracy over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('No normalization: batch size = ', 5)\n",
      "('Normalization: batch size = ', 5)\n",
      "('Normalization: batch size = ', 10)\n",
      "('Normalization: batch size = ', 50)\n"
     ]
    }
   ],
   "source": [
    "def run_batchsize_experiments(normalization_mode):\n",
    "    np.random.seed(231)\n",
    "    # Try training a very deep net with batchnorm\n",
    "    hidden_dims = [100, 100, 100, 100, 100]\n",
    "    num_train = 1000\n",
    "    small_data = {\n",
    "      'X_train': data['X_train'][:num_train],\n",
    "      'y_train': data['y_train'][:num_train],\n",
    "      'X_val': data['X_val'],\n",
    "      'y_val': data['y_val'],\n",
    "    }\n",
    "    n_epochs=10\n",
    "    weight_scale = 2e-2\n",
    "    batch_sizes = [5,10,50]\n",
    "    lr = 10**(-3.5)\n",
    "    solver_bsize = batch_sizes[0]\n",
    "\n",
    "    print('No normalization: batch size = ',solver_bsize)\n",
    "    model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=None)\n",
    "    solver = Solver(model, small_data,\n",
    "                    num_epochs=n_epochs, batch_size=solver_bsize,\n",
    "                    update_rule='adam',\n",
    "                    optim_config={\n",
    "                      'learning_rate': lr,\n",
    "                    },\n",
    "                    verbose=False)\n",
    "    solver.train()\n",
    "    \n",
    "    bn_solvers = []\n",
    "    for i in range(len(batch_sizes)):\n",
    "        b_size=batch_sizes[i]\n",
    "        print('Normalization: batch size = ',b_size)\n",
    "        bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=normalization_mode)\n",
    "        bn_solver = Solver(bn_model, small_data,\n",
    "                        num_epochs=n_epochs, batch_size=b_size,\n",
    "                        update_rule='adam',\n",
    "                        optim_config={\n",
    "                          'learning_rate': lr,\n",
    "                        },\n",
    "                        verbose=False)\n",
    "        bn_solver.train()\n",
    "        bn_solvers.append(bn_solver)\n",
    "        \n",
    "    return bn_solvers, solver, batch_sizes\n",
    "\n",
    "batch_sizes = [5,10,50]\n",
    "bn_solvers_bsize, solver_bsize, batch_sizes = run_batchsize_experiments('batchnorm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Y66EKfsRk+7KHDV+DjZLl+fPI8eeQ5tFYiIyPcalyaYzxAt8B3gE0AXuMMU9Zaw8nNftz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+HAyNtbhGVYURQXRJn2zFH77Wb7sCJrsLCK900Xt8IvVX5piAB2mB18qfJL49reJEzYzDZsZhtp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\nDeio0V2P6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j1VBPbuQ+vVXdss+fm4VqzQY+BWrMBeUYEwqxtrxZmhaZIjrb3sONrOm8c6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\nAFgpBR0AAMBKKegAAABWSkEHAACwUr8BcPr/STXJukEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faf297f08d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(2, 1, 1)\n",
    "plot_training_history('Training accuracy (Batch Normalization)','Epoch', solver_bsize, bn_solvers_bsize, \\\n",
    "                      lambda x: x.train_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n",
    "plt.subplot(2, 1, 2)\n",
    "plot_training_history('Validation accuracy (Batch Normalization)','Epoch', solver_bsize, bn_solvers_bsize, \\\n",
    "                      lambda x: x.val_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n",
    "\n",
    "plt.gcf().set_size_inches(15, 10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inline Question 2:\n",
    "Describe the results of this experiment. What does this imply about the relationship between batch normalization and batch size? Why is this relationship observed?\n",
    "\n",
    "## Answer:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Layer Normalization\n",
    "Batch normalization has proved to be effective in making networks easier to train, but the dependency on batch size makes it less useful in complex networks which have a cap on the input batch size due to hardware limitations. \n",
    "\n",
    "Several alternatives to batch normalization have been proposed to mitigate this problem; one such technique is Layer Normalization [4]. Instead of normalizing over the batch, we normalize over the features. In other words, when using Layer Normalization, each feature vector corresponding to a single datapoint is normalized based on the sum of all terms within that feature vector.\n",
    "\n",
    "[4] [Ba, Jimmy Lei, Jamie Ryan Kiros, and Geoffrey E. Hinton. \"Layer Normalization.\" stat 1050 (2016): 21.](https://arxiv.org/pdf/1607.06450.pdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inline Question 3:\n",
    "Which of these data preprocessing steps is analogous to batch normalization, and which is analogous to layer normalization?\n",
    "\n",
    "1. Scaling each image in the dataset, so that the RGB channels for each row of pixels within an image sums up to 1.\n",
    "2. Scaling each image in the dataset, so that the RGB channels for all pixels within an image sums up to 1.  \n",
    "3. Subtracting the mean image of the dataset from each image in the dataset.\n",
    "4. Setting all RGB values to either 0 or 1 depending on a given threshold.\n",
    "\n",
    "## Answer:\n",
    "3 is to batch normalization.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Layer Normalization: Implementation\n",
    "\n",
    "Now you'll implement layer normalization. This step should be relatively straightforward, as conceptually the implementation is almost identical to that of batch normalization. One significant difference though is that for layer normalization, we do not keep track of the moving moments, and the testing phase is identical to the training phase, where the mean and variance are directly calculated per datapoint.\n",
    "\n",
    "Here's what you need to do:\n",
    "\n",
    "* In `cs231n/layers.py`, implement the forward pass for layer normalization in the function `layernorm_backward`. \n",
    "\n",
    "Run the cell below to check your results.\n",
    "* In `cs231n/layers.py`, implement the backward pass for layer normalization in the function `layernorm_backward`. \n",
    "\n",
    "Run the second cell below to check your results.\n",
    "* Modify `cs231n/classifiers/fc_net.py` to add layer normalization to the `FullyConnectedNet`. When the `normalization` flag is set to `\"layernorm\"` in the constructor, you should insert a layer normalization layer before each ReLU nonlinearity. \n",
    "\n",
    "Run the third cell below to run the batch size experiment on layer normalization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before layer normalization:\n",
      "('  means: ', array([-52.24517198, -42.86302086, -37.18869497]))\n",
      "('  stds:  ', array([14.94837509, 25.33280868, 32.52972376]))\n",
      "\n",
      "After layer normalization (gamma=1, beta=0)\n",
      "('  means: ', array([ 2.22044605e-16, -1.66533454e-16,  5.55111512e-17]))\n",
      "('  stds:  ', array([0.99999998, 0.99999999, 1.        ]))\n",
      "\n",
      "('After layer normalization (gamma=', array([3., 3., 3.]), ', beta=', array([5., 5., 5.]), ')')\n",
      "('  means: ', array([5., 5., 5.]))\n",
      "('  stds:  ', array([2.99999993, 2.99999998, 2.99999999]))\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Check the training-time forward pass by checking means and variances\n",
    "# of features both before and after layer normalization   \n",
    "\n",
    "# Simulate the forward pass for a two-layer network\n",
    "np.random.seed(231)\n",
    "N, D1, D2, D3 =4, 50, 60, 3\n",
    "X = np.random.randn(N, D1)\n",
    "W1 = np.random.randn(D1, D2)\n",
    "W2 = np.random.randn(D2, D3)\n",
    "a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "\n",
    "print('Before layer normalization:')\n",
    "print_mean_std(a,axis=0)  # 这里有误, 原来是axis=1\n",
    "\n",
    "gamma = np.ones(D3)\n",
    "beta = np.zeros(D3)\n",
    "# Means should be close to zero and stds close to one\n",
    "# print('a.shape, gamma.shape, beta.shape: ', a.shape, gamma.shape, beta.shape)\n",
    "print('After layer normalization (gamma=1, beta=0)')\n",
    "a_norm, _ = layernorm_forward(a, gamma, beta, {'mode': 'train'})\n",
    "\n",
    "print_mean_std(a_norm, axis=0) # 这里有误, 原来是axis=1\n",
    "\n",
    "gamma = np.asarray([3.0,3.0,3.0])\n",
    "beta = np.asarray([5.0,5.0,5.0])\n",
    "# Now means should be close to beta and stds close to gamma\n",
    "print('After layer normalization (gamma=', gamma, ', beta=', beta, ')')\n",
    "a_norm, _ = layernorm_forward(a, gamma, beta, {'mode': 'train'})\n",
    "print_mean_std(a_norm,axis=0) # 这里有误, 原来是axis=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('dx error: ', 1.7029255488709078e-09)\n",
      "('dgamma error: ', 7.420414216247087e-13)\n",
      "('dbeta error: ', 2.8795057655839487e-12)\n"
     ]
    }
   ],
   "source": [
    "# Gradient check batchnorm backward pass\n",
    "np.random.seed(231)\n",
    "N, D = 4, 5\n",
    "x = 5 * np.random.randn(N, D) + 12\n",
    "gamma = np.random.randn(D)\n",
    "beta = np.random.randn(D)\n",
    "dout = np.random.randn(N, D)\n",
    "\n",
    "ln_param = {}\n",
    "fx = lambda x: layernorm_forward(x, gamma, beta, ln_param)[0]\n",
    "fg = lambda a: layernorm_forward(x, a, beta, ln_param)[0]\n",
    "fb = lambda b: layernorm_forward(x, gamma, b, ln_param)[0]\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(fx, x, dout)\n",
    "da_num = eval_numerical_gradient_array(fg, gamma.copy(), dout)\n",
    "db_num = eval_numerical_gradient_array(fb, beta.copy(), dout)\n",
    "\n",
    "_, cache = layernorm_forward(x, gamma, beta, ln_param)\n",
    "dx, dgamma, dbeta = layernorm_backward(dout, cache)\n",
    "\n",
    "#You should expect to see relative errors between 1e-12 and 1e-8\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dgamma error: ', rel_error(da_num, dgamma))\n",
    "print('dbeta error: ', rel_error(db_num, dbeta))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Layer Normalization and batch size\n",
    "\n",
    "We will now run the previous batch size experiment with layer normalization instead of batch normalization. Compared to the previous experiment, you should see a markedly smaller influence of batch size on the training history!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('No normalization: batch size = ', 5)\n",
      "('Normalization: batch size = ', 5)\n",
      "('Normalization: batch size = ', 10)\n",
      "('Normalization: batch size = ', 50)\n"
     ]
    },
    {
     "data": {
      "image/png": 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rllkKZfeMnceWunzyhUdkzhhr7fVbzaHKykpbXV0d626IiMgSc9//ex9X+65O\neD7Vmxq13H94XltaCcme5DnsqYjIKCefGbtqo8sDpTvA7RmZ1+YfiH5dcm50SIs8phVB3OJeaMnX\n3Ez9579A0VPfIC43N9bdiWKMOWqtrZxKW1XoRERkSQoEA5xqPcWL9S/yYt2Lk4a5Fz7wAhnxGZrX\nJiLzR18bNJ2Eptfhl38WHeYAgj64+Dzk3wY5a6D8fiesZZSEglsJeJf2L6Ouffs79B89Ssu3v83y\nJ5+MdXdumgKdiIgsGR0DHRxsOMhL9S9xsP4g7YPtuIyLDTkbSPWk0u3rHvOa5cnLyUzIjEFvRURw\nhkp21UPjSSfADR/H2xh7PJ96aXb7t0AEe3sZrLnIUM0FBi/U0H/mNH0vHQRr6fy3fyf3t3973lXp\npkqBTkREFq2gDXK27Swv1r3Ii/Uv8vq11wnaIJnxmews3Mmuol1sL9hOenz6mDl0AAnuBPZW7I3h\nHYjIkhIMQOv5UGh7LXR8PWIFSQPZq6FoC2z5TcjfAMs3wnd3jx/w0ovmsvcxZ60l0NbG4IULDNXU\nMHihhqELFxi8eBF/Y8SiLnFxuBITo17X8u3vsPxJbSwuIiISc11DXbzc8DIv1r3IS/Uv0TrQisFw\nW85tfHLDJ9lVuIv1OevHrDA5vJrlVFa5FBGZNt8ANJ8aCW1NJ+HqKfD1OefdXshbC7fucUJb/gZn\nz7b4cfaivPeJsXPoPInO84uQDQbxNTQ4YS2i6jZ04QKBzs5wO5OURPzKlSRtqSS+bBXeVWXEl5Xh\nSkzkwjsfdKqfAD4fnf/+7+T+9qcXZJVOgU5ERBY0ay1vtb8Vngv3WstrBGyANG8aOwp2hKtw2YnZ\n173WnrI9CnAiMvP626Hpjeghky1vgg045+PTIP92qPgoLN/ghLfcW5wFTaZieDXLyVa5XICCQ0MM\nXbrEUM1FBmsuMHShhsGaGoYuXsQOjIymcGdlEV9WRuoDDxBfthJv2SriV5URl5+PGWclzsZ9f4QN\nBqOes8Hggq3SKdCJiMiC0+vr5ZWGV5wQV/8izX3NAKzNWstv3PYb3FV0F7fl3EacS//NicgcstbZ\nry083+0159hxZaRNSr4T2m55MBTebnf2bpvuFgAbHlmwAS7Q0zMyRDKi2jZUVweBQLidp6AA76pV\nJFdVOdW2VavwlpURl3lj85z7T5wAny/6SZ+P/uPHZ+J25ty0/qczxjwAPA24ge9Za786TptHgH2A\nBV6z1n7PqZ0zAAAgAElEQVRoOu8pIiJLj7WWms4aXqp/iRfrXuRo81H8QT8pnhS2FWxjV+Eudhbu\nJDdp4Q2VEZEFKhiEtgsjoW146GTftZE2Waug8A6449dD8902QEpe7PocQ9ZaAteuMXihJqLa5hz9\nzc0jDT0evKUlxN9yC6kPvpP4ULXNu3Jl1Ly36Sj7yY9n5DrzxU0HOmOMG/gW8HagDjhijHnWWns6\nok058PvADmttuzFmaf4JFhGRG9bn6+NI05HwUMqG3gYAyjPLeWzdY+wq3MWmvE14XNq4W0RmmX8Q\nmk87gW24+tb0Bvh6nfMujzPfbc0DI0Mm82+D+NTY9jsGbCCAr74+emGSGmeoZLCrK9zOlZTkVNu2\nbcO7aji0leEtLsJ49O/6jZhOha4KOG+trQEwxvwQeDdwOqLNbwHfsta2A1hrm8dcRUREJORy1+Xw\nipTVTdUMBYdIjEvkzuV38vENH2dX4S7yk/Nj3U0RWcwGukYWKQnPdzsLQb9z3pviDJPc/JGI+W63\nLvpNuEcLDg4689tGL0xy6RJ2cDDczp2TQ3xZGWmhatvwUMm4Zcu0t+cMmU6gKwQi10etA7aOarMG\nwBhzEGdY5j5r7c9GX8gY8wngEwAlJSXT6JKIiCwkA/4Bqq9Wh1ekvNLtzDNZmb6SR299lF1Fu6jI\nq8DrXlo/KInIHOluGrtFQPvFkfPJeU5oK79/JLxlrpz+fLcFJNDVFaq2RS9M4qurc4adAhiDp7AQ\n76oykrdvd6ptZauIL1uJOyMjtjewBMz2bPE4oBzYDRQBLxhjbrfWdkQ2stZ+F/guQGVlpZ3lPomI\nSAzVddc5c+HqX+Rw42EGAgMkuBOoWl7FR9Z9hJ2FOylOLY51N0VkMQkGnaDW+Fp09a03YvBY5kon\ntG3+yMh8t9SlMSLAWou/uWWkyhY6DtZcINAyMifQeDx4V6wgYd060t/1rpGFSVaswJWQEMM7WNqm\nE+jqgcj/cYtCz0WqA1611vqAi8aYt3AC3pFpvK+IiCwgQ4EhjjUfCw+lvNjp/Pa7OLWYXyn/FXYV\n7aJyWSUJcfphQERmgH/IGSIZOWSy6Q0Y6nbOu+Igdy2Uv90ZOjk83y0hPbb9ngG+5mbqP/8Fip76\nxrj7qVm/H19dHYM1NU7VbXgbgJoagj094XaulBS8q8pI2bkrVG1z9m/zFBVh4rR68Hwzne/IEaDc\nGLMSJ8g9CoxewfInwAeBvzPG5OAMwayZxnuKiMgC0NTbFF7M5JXGV+j39+N1eanMr+SRNY+wq2gX\npWmlse6miMxnJ5/B9+wfUf/zfooeSCTuoSfHLss/2D1qf7fXoPksBENL0nuSnbC28dGRIZN5ayEu\nfu7vZw5c+/Z36D96lOa/+iZZH3w0ehuAmtD8tojl+uNyc/GWlZH+8EPhvdu8ZauIy8vV/LYFxFh7\n8yMcjTEPAv83zvy471tr/9QY82Wg2lr7rHH+JPwF8AAQAP7UWvvDya5ZWVlpq6urb7pPIiIy93xB\nHyeaT/BivTMX7lz7OQAKkgvYVbSLXYW72JK/hSRPUox7KiILwsln4LnP0viyh47zyWSs7mX5nUNQ\n9UlITMc2vAaNr0PrRay1YA02MRvy1mNz1kLuWueYWogFCASwgSAExzsGIBic/Djha693dF5rg1M7\nTnitQMDZCHuSY3BwkKELF5y98CK5XHiKiogvK3OGSIaDWxnutLRYfHdlCowxR621lVNqO51ANxsU\n6EREFoaWvpbwXLiXG16mx9dDnInjjmV3sKvI2ReuLL1Mv+UVkbECfhjsgoHO0LErfLQDnQz9+Ct0\nngvQejoFMICF4XVILGAX2L8rLhe43ZiZPrpd4HKD28XgufP4GxudQOdykbxtG3m/+0Vnflv84qxI\nLmY3Eug0CFZERKbEH/Tz+rXXwytSnmk7A0BeYh7vWPEOdhXuYuvyraR4U2LcUxGZVf6hCcNY1HGy\nc76+qEsGA9DXHE9PQzw9DQn4eoer+SOFh/g0HynLh2DHZzDeJHC7MG43uEaCzVSPI6+93nGCa9xA\n6MLlmvVfbPmam7nw9vtHqnPBIH3V1cRlZSnMLQEKdCIiMqHW/lYONRzixboXOdhwkK6hLtzGzcbc\njeyt2Muuwl2syVyjKpzIQuEbiAhWnaFj9zihq3PiMOYfuP77eJIgPg0S0kaO6UURj9Px9bnoOXOV\nnhMX6T15HjswiImPJ3lrJRmDB7l2xGKDw/+2GIa6PWQ9lEDc//rfs/olWoiuffs7ztDLCDYYpOXb\n32H5k0/EqFcyVxToREQkLGiDnLp2KrygyanWU1gs2QnZ3FN8D7uKdrGtYBtpXs27ELlhJ5+BX3wZ\nOuuccHPvE2MX+ZiIteDrHxW+JgldE1XPAkPXfy9vSnQYS8qCzBXR4Sw+feRxfGpUUCM+FdyesbcQ\nDDJw6hQ9B56n58ABBk6dAiCuYDkZv/IrpNx9N0lbt+JKSKDxs49hRy2Kbi20XL2D5VP7ii0p/SdO\nQMRiJwD4fPQfPx6bDsmcUqATEVniOgY6ONRwiJfqX+Jgw0HaBtowGDbkbuDxTY+zs2gna7PW4jJL\nZyNdkRkXWuQDX7/zuLMW/uNxuPgC5KyZQijrHlm5cTLxo0JWci5krxpVLUsfWz2LPLrcM3bbgZ5e\nel8+RM+BA/Q8/wKBa9fA5SJx0yZyP/c5UnbvJn5N+Zgqf/+VHgiOqvwHDf1Xumesb4tJ2U9+HOsu\nSAwp0ImILGL7a/bz9LGnaeptIj85n70Ve3nnyndytu2ss6BJ3YucvHaSoA2SEZ/BjsId7CrcxfaC\n7WQmZMa6+yILj7XQ1wat56HtgnNsPQ9nfzo2kAWG4Pg/hh6YseEqdTnk3jJO6EofP4x5U53FN2Js\nqLaWnl8eoOfAAfqOHMH6fLhSU0nZtZOU3btJ3rWLuMzJ/31RQBGZOq1yKSKySO2v2c++Q/sYCIzM\nd3EbN4lxifT4nA1k12evD28rsD57Pe4Z/M28yKI21Autw4EtIri1noeBjpF2xu0MV2y7MMGFDPze\nFWeY4zwIYzfD+nz0HT/uDKV8/nln6XzAW1ZGyu7dpOy+m6TNmzGescMwRWR8WuVSRGSJGwwM8rUj\nX4sKcwABG8Af9POnO/+U7QXbyUnMiVEPRRYA/xB0XI4Oa60XnI/uhui2aYXO0MbbfgWyV0PWKueY\nWerMJ3vqNmeY5WjDC4UsMP72dnpfesmpxL30EsGuLvB4SN6yhcwPfICU3XfjLSmJdTdFlgQFOhGR\nRWAoMMTJlpMcuXqEI01HeK35NYaC4y9+MBgY5OFVD89xD0XmqWAQuuqjA9vwUMn2y2ADI20Ts5yQ\nVrYbssucz7NXQ1YZeJMnf597n4ieQwfgSXSeXwCstQyeOxde0KT/xAkIBnHn5JB6332k7L6b5O07\ncKdc5+sgIjNOgU5EZAHyBXycaj3F4abDHG46zGvNrzEQGMBguDXrVh699VGeu/Ac7YPtY16bn5wf\ngx6LxJC10Nc6zvDIUHiLXIbfk+RU2vI3wPpfGQlt2auc1R5v1vBqlje7ymUMBAcH6Xv1VXoOHKD7\nwAH8DY0AJKxbR86nPkXKPbtJWL/e2W9NRGJGgU5EZAHwB/2cbj3N4abDHGk6wvHm4/T7nd/0r8lc\nw/vWvI/K/Eoql1WSHp8OwLrsdWPm0CW4E9hbsTcm9yAy6wZ7IhYiGT2vrXOknSvOmdeWvRpW3eOE\nteHglrocZmtfxQ2PzOsAB+C7ejU8F6735Zex/f2YxESSt28n5dOfJuWuu/Esy4t1N0UkggKdiMg8\nFAgGONt2NhzgjjUfo9fXC8DqjNW8e9W7qVpeReWyyglXo9xTtgdgzCqXw8+LLEj+IWi/NHZeW9sF\n6G6MbptWFJrX9r7oSltGybj7pC1FNhhk4I03wlW4wdNnAPAUFjp7w+2+m6SqKlzx8THuqYhMRKtc\niojMA0Eb5M22NznS5MyBO3r1KN0+Z7+lFWkrqMqvYsvyLWxZtoXsxOwY91ZklgWD0FU3qtIWOnZc\nBhscaZuUHR3WhhckySoDb1Ls7mEeC/T00HswtDfcCy8QaG119oar2EzK3XeTuns33tWrx+wNJyJz\nR6tciojMc0Eb5HzHeY40HeFw42Gqr1bTNdQFQElqCfevuN8JcflbyE3KjXFvRWaBtdB7bex+ba0X\noK1m1Ly2ZCesFWyG298XvRjJdOa1LSFDly+Hq3B91UfB58OVnk7KTmdvuJRdO3FnZMS6myJyExTo\nRETmgLWWms6a8BDK6qbq8IIlhSmF3FtyL1vyt7Alf4sWLZGF4+Qz11/kY7B7gv3aLsBg5Lw2D2St\ndKprq94WUXVbDan5szevbZGyPh99R485Vbjnn2fo4kUAvKtXkf3RXyNl924SN23CxOlHQZGFTn+L\nRURmgbWWS12XnApcKMS1DbQBsDx5ObuKdrElfwtV+VUUpBTEuLciN+HkM9HL8HfWwn88Dm/9zFnC\nfzi89VyNfl16sVNt2/D+6KGS6SXg1o8l0+Fvb6f3hRfoPnCA3hdfItjTg/F4SNq6lcwPf9jZG66o\nKNbdFJEZpn85RURmgLWW2u7a8DYC1U3VtPS3AJCXmMe2gm3hIZRFKUWamyILj7XOoiPXzkHrOfjv\nJ6P3VAMIDMEb/wZJOU5QW/326BUks1Y6e6/JjLDWMvjWW87m3gcO0P/aa2At7twc0t75ACl3303y\ntm24krU3nMhipkAnInKT6rrrwouYHG46zNU+pxKRnZAdXsSkKr+KktQSBThZOAZ7oleQHA5wrRdg\nqGcKFzDwuxdmvZtLVXBggN5XXgkNpXwBf2Nob7jbbyfn8cdJ2b2bhHVrtTecyBKiQCciMkVNvU1O\nBa7RGULZ0NsAQFZCFpXLKsMhbmXaSgU4md+CAWeI5LXzTlgbDm3XzkN3Q0RDAxnFkF0OxXdCTrlT\nacsph++/w5k7N1q6hvTNNF9jIz3PP0/PgefpfeUV7MAArqQkkndsJ+Uzj5Ny113E5WrxJJGlSoFO\nRGQCzX3N4flvR5qOUNtdC0B6fDpblm3ho+s/ypb8LazO0PLeMk/1t48f2tpqIDA40i4+HXJWw8q7\nnGN2uRPassomHiJ575PRc+jAaXvvE7N7T0uADQToP3kyHOIGz54FwFNcTMb73+/sDbdlCy6vN8Y9\nFZH5QIFORCTkWv+18PDJ6qZqLnVdAiDVm0rlsko+eOsHqcqvojyzHJfRcCaZJwI+aLsYHdpaLzif\n910baeeKg8wVTlgrv28ktGWXQ3LOja8iObya5fVWuZQovuZm6j//BYqe+kZUVS3Q3U3vwYPOfLgX\nXiDQ3g5uN0kVFeR98Yuk3LMb70pV/0VkLAU6EVmy2gbaqG6qDlfhajprAEj2JHPHsjt435r3sSV/\nC7dk3oLb5Y5xb2VJsxZ6mkNhbXheW+jYfglsYKRtcq4T0m590DkOD5HMXAFuz8z2a8MjCnA36Nq3\nv0P/0aO0fPs7ZP3aY/QceJ6e55+nr7oa/H7c6ekk33UXKbvvJmXnTtzp6bHusojMc8Zae/MvNuYB\n4GnADXzPWvvVUec/BnwNqA899U1r7fcmu2ZlZaWtrq6+6T6JiEykY6CDo1ePhleiPN9xHoDEuEQq\nllVQlV9FVX4Vt2bdSpxLv++SGPD1h5b7PzdqqOR5GOwaaeeOH1k9crjKNjy/LVGbQ8831loCHR30\nv/46db/9OPj9TkU09DNYfHm5s7n3PbtJ3LgR49YvkESWOmPMUWtt5VTa3vRPLMYYN/At4O1AHXDE\nGPOstfb0qKb/aq39zM2+j4jIzeoa6uJo09FwBe6t9rewWBLcCWzO28yesj1syd/Cuux1eFwzXLkQ\nmUgwCF3144e2ztrotmmFTkjb8EgotIXmt6UXg1YxnFfs0BC+hgaGauvw1dU6x9pahuqcY7Bn7Aqh\niZV3UPjnf46nsDAGPRaRxWI6v4KuAs5ba2sAjDE/BN4NjA50IiIzZn/Nfp4+9jRNvU3kJ+ezt2Iv\ne8r2ANAz1MOx5mPOKpRXj3C27SxBGyTeHc+m3E08vulxtuRv4fac2/HM9NAzkdEGuqJDW+v50Ofn\nwR+xkIg3xQltJXdC9mOh0Bb68Gr/sPnCWkugrc0JaeOENn9TU7jiBmC8XjxFRXiKi0iqqMCVmUHr\n33wXfL7hCzLw+hsYLWwiItM0nUBXCET+KrEO2DpOu181xtwFvAV8zlpbO7qBMeYTwCcASkpKptEl\nEVnM9tfsZ9+hfQwEBgBo7G3kiYNP8J8X/5O2gTZOt54mYAN4XB425m7kkxs+yZb8LWzI3UC8Oz7G\nvZcF4eQzN7bIR8APHZdHKmyRAa7n6kg744KMUmdY5Mq7nOGSw0MlU/NvfEESmRXBgQF89fUM1dbi\niwxtdXUM1dVh+/qi2sfl5uIpLia5agueomI8xUV4i4vxFBUTl5sTtRdc474/GvN+Nhik5dvfYfmT\nWhlURG7ebE8SeQ74F2vtoDHmk8A/AG8b3cha+13gu+DMoZvlPonIAvXU0afCYW7YUHCI5+ueZ3Pe\nZn7z9t+kKr+KjbkbSYhLiFEvZcE6+Uz0Mvydtc5jgFVvi95kezi0tV2EoG/kGomZTkhbfV/0/Las\nlRCnXyrEmg0G8bdcCwW1iNBWV4+vthZ/c3NUe5OYiLeoyAlt2+4cCW1FRXgKC3ElTrClwzj6T5wY\nqc4N8/noP358Jm5NRJaw6QS6eqA44nERI4ufAGCtbY14+D3g/5rG+4nIEtPY08ix5mMcbz7OseZj\nXO27Om47g+EH7/zBHPdOFg1rnf3a/utL0XuqgfP43z8BRPyu0eVx9mfLWQO3PBi9KElS1px2XcYK\n9vY6Aa2u1qmsRc5lq6vDDkbsv2cMcfn5eIuKSN65E29xkRPaigrxFhfjzs6esW0Cyn7y4xm5jojI\naNMJdEeAcmPMSpwg9yjwocgGxpjl1trG0MOHgTPTeD8RWcQCwQDnO847Ae5qdIBL9iSzMXcjjT2N\n9PjGLiyQn5w/192V+S4YgL5WZ6n/3hbno6cZepuhpyV0jDgX9E9yMQvv+LOReW0ZpeDWKqixYgMB\n/FevhhYbqWOoLlRpC4W2QGtrVHtXcjKekhLiy8pIufvucFjzDFfZNIdNRBa4m/4fyVrrN8Z8Bvg5\nzrYF37fWnjLGfBmottY+C3zWGPMw4AfagI/NQJ9FZBHo9/fzxrU3OHb1GMdbjvNa82vhsJaXmEfF\nsgo2522mYlkF5RnluF3uMXPoABLcCeyt2Bur25C5FPBB77XxQ9nosNbXCjY49houD6TkOXu1peZD\n/gZIyYXkPHjh69DfOvY16cWw7fHZvz8JC3R3h6prtWNDW0ND9NBFtxvP8uV4iotIfds9eIqKnUpb\nKLS5MzK0GbeILGrT2oduNmgfOpHFqbW/lRPNJzjWfIwTzSc43Xoav3WqIqszVlORV8HmZZvZnLeZ\nguSCCX8Am2yVS1mA/IMTV85GV9f628a/RlziSCgbDmspeaHHkc/nQELGxAuQjJ5DB+BJhIf+Uptn\nT8LX3Ez9579A0VPfIC43d0qvsX4/vqam0IqRY0NboLMzqr07PT20YmRxeFhkOLTl52M8WrVWRBaX\nG9mHToFORGactZbLXZc53nw8/HGp6xIAXpeX23JuC1fgNuZuJD0+PbYdlpk11DvOUMfxhjy2wGDn\n+NfwpkaEsVFhLRzYQkdvysytEnmjq1wKjfv+iI5//VcyHn00vFqjtZZgZ+eY5f199aE5bQ0NEAiM\nXCQuDk9hAd5RK0U64a0Id1pajO5ORCQ2FOhEZE75gj7Otp4NL2ByvPk4bQNONSU9Pp3NuZvZvGwz\nFXkVrMteh9etOStzZiYCirUw2D0qlDVPHNZ8veNfJyFjVOVsgsCWkudUxmTe89XXc+GBd2J9PnC7\nSd65E39LM77aOoLd3VFt3VlZoRUix4a2uGXLMG53jO5CRGT+uZFAp1ndInLDeoZ6eK3ltXCAe73l\n9fC8tqKUInYW7nTmv+VVsCJ9BS7jus4VZVZMtgz/7e93Vna83mIhw8/5B8Z5AwNJ2aEQlguFlRMP\neUzOhTgF+YXO+v0MvPEGvYeP0Pfqq/S+8spIpS0QoP/YMRIrNpO0uWJkef/iYjyFRbhTtEm6iMhs\nUIVORK6rqbfJ2Trg6jFOtJzgrfa3CNogLuPi1qxbnflvec78t9ykqc2hkTnwjXXQVT/2eeN2NroO\n+sY/l5wzqnI2wfy0pGyt9rjI2UCAgdNn6Dv8Kr2vvkp/9VGCoc21vStXMHSlNmropImPZ/X//PeU\n59KJiMj4VKETkZsWtEHOd5zn+NXjHG85zvGrx2nobQAgMS6Rjbkb+eSGT7I5bzMbcjeQ7NFv3eeF\nYBBazkLdYag94hzHC3MANgA7Pjt+SEvMBJcqqkuVDQYZPHuW3lcP0/fqq/RVVxPscVaf9ZaVkfbu\nh0neupWkLVto+atvMlRXHxXobDBIy7e/E55LJyIis0+BTmSJGwwM8sa1N6IqcN1DztyXnMQcNudt\n5rF1j7F52WZuybyFOJf+2ZgX+tuh7mgowB2G+qMw2OWcS8yEoirovjr+oiPpxXDfvrnsrcxTNhhk\n8Nw5Z/jkq4edABdaYdJbWkragw+SVFVFUtUWPHl5Ua/tP3EievsAAJ+P/uPH56r7IiI37SfH6/na\nz9+koaOfgoxEvviOW3jP5sJYd+um6CczkSWmfaCdE80nnADXfIxTrafwhzZVLksv4/7S+8MrUBal\nFGn/pvlgvOrbtbecc8YFeevh9vdB0RYnyGWvclZ9nGgZ/ntVPVmqrLUMnT/vhLfDzkegowMAT3Ex\nqffd61Tgqqrw5OdPeq2yn/x4LrosIjLjfnK8nt//99fp9zkjDOo7+vn9f38dYEGGOgU6kUXMWktd\nd13U6pM1nTUAxLniuC37Nqf6lruZTXmbyEzIjHGPBQhV36qdylvdYag/FlF9y3KC24ZHnPBWWAHx\nqeNfZ3g1Sy3Dv2RZaxm6eCk8B67v8BECrc7m6XEFy0nZvZukrVtJrtqCp3Dh/RAjIjIsELT0Dvnp\nHXQ+egYD9A366Rn00zvkPB4+9/2DF8Nhbli/L8DXfv6mAp2IxJY/6OfNtjejAty1/msApHpT2ZS7\niYdWPcTmvM2sz15PQlxCjHssBAOh6tuR61TfqqC4CrLKbmzPtQ2PKMAtIdZafFeuOOEtVIXzt7QA\nELdsGck7to9U4IpUgReR2LHW0jcUCIUvP72DgXAgCz8Ofx4dysLPDY48NzqgTcRlIDjBmpANHf3j\nn5jnFOhEFrBeXy+vtbzGieYTHGs+xsmWk/T7nX+MCpIL2Lp8a3gFylUZq7R9wHzQ1+bMdxuuvtUd\nhdCcRRKznNC24QPOsWDzxNU3kZChujpnAZPDh+l99TD+piYA3Lk5JG+pcipwW6vwlJYqwIksYrM9\nJ8xay6A/GA5To0NYZLiKDF3h54Yig5jzuqkutp/kdZMcH0dKfBzJ8W6SvXHkpyWQHB8Xej7yfOjD\nO/o5NynxcSR63Oz8819SP054K8hYmHugKtCJLCDNfc3hytuxq8d4s/1NgjaIwXBL1i28Z/V7qMir\nYFPeJvKTJ5//InNguPpWezhUgTsMreecc8YFy9Y71bPiKmcY5Y1W32RJ8jU00Hv4sFOBe/VVfA3O\nKrTurCySqqpI3uqEOO/KlQpwIkvERHPC/IEg965dFh522Dtu5SswKqQ5baKrYE67wESlrVHi41xR\n4Sol3k1WspfirCRSvCMBKyqQeeNGvcZpk+SNw+2a2X/LvviOW6K+XgCJHjdffMctM/o+c0X70InE\n2P6a/Tx97GmaepvIT85nb8Ve9pTtIWiDXOy86AyfvOosYFLf4yxDn+BOYEPuhvDebxtyN5DqVSUn\n5vranLlvdaEAF1l9S8p2hk0WVYaqbxUQnxLb/sqC4LvaPDIH7tXD+GprAXCnpzsrUIYqcN7VqxXg\nRJagzn4fb/v6AVp7h276Gh63CVW1IqpgoceRFbDIite4z3njSIp343HP/xFB832VyxvZh06BTiSG\n9tfsZ9+hfQwEBsLPxZk4yjPKaehroDO05HxWQlY4vFXkVXBr9q14XJ5YdVvAqb41nwkFt9HVN7dT\nfSvaouqb3DB/S8tIBe7wYYYuXQLAlZZG0pYtTgWuqor4NWsw2jNQZEnxBYKcbezmRG07x2s7OFHb\nQU1L76SveeJd68YMOxw9FDE+zj1HdyBTpY3FRRaIp44+FRXmAPzWz1sdb/HwqoedALesgpLUEv3m\nPdYiq2+1oZUnR1ffNn3QORZsVvVNpszf1kbf4SOhKtxhhi5cAMCVkkJSZSUZjzxC0tYqEm69FePW\nD10iS4W1lrr2fie4XengRG07pxq6GPQHAchJiWdTcQa/WlHE3x28yLWesRW6woxEfmPnyrnuuswx\nBTqROeYP+jnUcIhnLzzL1b6r47YJ2iBf3vHlOe6ZhIWrbxH7vrWed84NV982fiC08uQWyFyp6ptM\nWaCjg94jR8Jz4AbPOZVdk5RE0h13kPHe95C0dSsJa9di4vTftMhS0dnv47VQ1W34ODyMMsHj4vbC\ndH5tWykbizPYVJxBYUZi+Je9hRmJi2pOmNwY/U8hMkfebHuTZy88y/6a/bQOtJIen05SXBJ9/r4x\nbbWgyRzra4seOhlVfctxhk1u+rAzdLKwArzJse2vLCiBri76qqvpe9WpwA2++SZYi0lIIKmigrR3\nvYvkrVUkrF+P8WgotchSMOQPcrapi9dqO8YMnTQGVuWmcM+teWwKhbdb8lMnnZc2PPdrPs8Jk9mj\nQCcyi1r7W/npxZ/y7IVnOdt2ljgTx66iXbx71bu5q+gu/uvyf42ZQ5fgTmBvxd4Y9nqRu171Lf82\n2PhoaP6bqm9y4wI9PU6AO3yEvldfZeDMGQgGMfHxJG7eTO5nf4ekqioSb78d4/XGursiMsvGGzr5\nRtjxyucAACAASURBVEMXQ+MMndxUnMHtRemkJdz4L3fes7lQAW6JUqATmWFDgSEO1B7g2QvP8lL9\nSwRsgHXZ6/i9qt/jwZUPkpmQGW67p2wPwLirXMoMGa6+De/7Vn8Mhnqcc5HVt+F931R9kxsU7O2l\n79jx8By4gVOnIBDAeDwkbtxIzqc/TdLWKhI3bsQVHx/r7orILIscOjk8fHL00MmPbitlU3EmG4vT\no4ZOitwMrXIpMgOstZy8dpJnzz/Lzy79jK6hLvIS89izag8Plz3M6szVse7i4nHyGfjFl6GzDtKL\n4N4nnL3cIFR9Oz2y71vdkbHVt6KqkZUnM1eo+iY3LNjfT//x4/SG5sD1v/EG+P0QF0fihg0kba0i\neetWEjdtwpWQEOvuisgsGh46eWK4+lY3dujk8LDJqQydFBmmbQtE5khjTyPP1TzHcxee41LXJRLc\nCbyt5G08vOph7lx+J26XVqSbUSefgec+C77+kefcXlh9n1N1i6y+JeeOLFoyvPKkNyk2/ZYFxdfc\nTP3nv0DRU98gLjeX4OAg/cdPhCtw/SdPgs/H/8/enYdHWd1/H3+fWZLJHkhCEpIgSxBkCTsqiCLK\nokjcrUWsilaromirVeujUqxCK78qVrS1ilq1UhfEsKgoi4qo7DsoSQhkJwQySSCTzHKeP2ayJyzZ\nJhm+r+vimpmTM/d9bmYC85mzYTQSMGAAgeefT+D5IwkcMgRDoLzHhPBVWmsyj5axNfMY2zOtjQ6d\nHNItvFlDJ4WANgx0SqlJwHzACLyhtZ7bSL3rgY+BEVrrk6Y1CXSivTthP8HXh74mJTWFDXkb0GiG\nRQ8juVcyE86ZQLDfGS5Xf7Iep47K5QJnOTjKwVlR57YcHBV1bk+z3rYPwN7Ifjuxg6t73qT3TTRD\nzlNPY/34Y/wH9McYEEjZtm3oigowGLD071+1D1zA0GEYg2WIrhC+ynrCzrasolorT9YdOunueZOh\nk6Lltck+dEopI7AAGA9kARuVUila6z116oUAM4GfmnouIbzNpV1szNtISloKXx38ijJHGfHB8dw7\n6F6u6nUVCSEJTTtw3R4na6b7MZx+qNO6ZUNTg/VOVr/O85wV4HI07e+jIQYzmPzdPXGNhTkU3PNN\ny51TnBVcFRVUHMigPHU/5ampVKSmYtv3M/bMTADKd+7C79zedJo61d0DN3w4xpAQL7e6fVmyNVtW\n1RM+od7Qycwi0o9UD51MjApmXN8uVVsGyNBJ0Z40Z1GUkUCq1jodQCm1CLga2FOn3rPAX4FHm3Eu\nIbzigPUAS9OWsjR9KXnH8wg2B3NljyuZ0msKQ7sMbf43cV/Pqj18ENyPUx6Abe+fXthy2ZvXhpqU\nsTo8mfzB6A8mv/q3/iF16tW9beh5/mA0N1B2kuca/cBQ4z/MFwe4Q29dYfEt93cgfI6uqKA8I4OK\n1FTKU9MoT011B7iDB8Hp2bPJYMCvWzfQLvd7zuUCs5nAYcOJfvwx715AO7Vka3atfa+yi8p4YvFO\nAAl1ol2rOXSycuGS3Q2tOjmseatOCtFWmhPo4oCan6yygPNrVlBKDQUStNbLlVKNBjql1N3A3QDd\nunVrRpOEaD5ruZUvDnxBSloKO47swKAMXNj1Qh4e+jDjuo3DYmrGIgcuJ+Rsg7TV7j/F2Q3Xc9ig\n4oQ74JjDzzA0+XnqnGZoqlmvvc/5u+zp+nPozAHucnHW03Y7FQcPugPb/tTawc3h6TU2GPBLSMCv\ndyIhE8bjn9gb/96J+PXogbOoiLTxE9xhDsBux7p4MVH33YspKsp7F9YOuFya4xUOSssdlNoclJQ7\neHbZnlqbGAOU2Z387Yt9EuhEu1I5dLJyy4DtWVaONrLq5OBu4XQNs8jQSdGhtNq2BUopA/B34PZT\n1dVavw68Du45dK3VJiEaY3fZ+T77e1LSUlibuRa7y05ieCK/H/Z7JvecTJfALk0/eNGh6gCX/g3Y\nigAFsYPcPV3lJfWfE5YAd33V9HP6qsphqL4251CcEW23U3HoUK3QVp66n4qMGsFNKczdEvBP7E3I\n5Zfjn5hYFdwa2zog/9XX0JVhrvJcLhcFr75G7DMd80sDu9NFqc0dxEpsDnco8wQyd7m9zuPquqV1\nyk5XjtXG8L98RUyYhdiwALqGWYgNDyA2zEJXz210qEWGq4lWUeFwsTe3uGrOW0NDJy/r24XB3cIZ\nFC9DJ4VvaE6gywZqThyK95RVCgEGAGs933LEAClKqeRTLYwiRFvQWrPv6D5S0lJYcWAFR21H6eTf\niZv63ERyr2TO63xe076hsxVDxjpPgFtTvWx+aBycdxX0vBR6joWgyIZXbZQep5NLukkC3FlCOxw1\nglv1PLfyjIPuVSbBHdwSEvBPTCRk3GX4J/bCPzERv549z3jLgLJt26qPW8lup2zr1ha6otOjtabc\n4aoVqko8wauxwFVSGc7qlJU7XKc8n1IQ7G8ixN9EsMVEsL+J0AAzceEBBHvKgur8PNhi4tGPtnOk\ntKLe8UIsJsb3iybXauNQ4Ql+TC+kxOaod84uIf7uwBfuDn6xngAYG26ha1gAUSH+GA3SS3I2O9Uc\nTa01h46eqBo2WXfoZFSIDJ0UZ4cmr3KplDIBvwCX4Q5yG4GpWuvdjdRfCzwiq1wKbztSdoTl6cv5\nLO0z9h/bj8lgYmz8WJJ7JXNR3EWYjWf4j73LCTlbIW2NO8RlbXAvCmIOhO4XQa9x7j+R5za86qIv\nrnIpxBlwB7dMdy9bWlpVz1vFgQPomsEtPt7d01YZ2hIT8e/ZE0NAQIu0o+6cMIAAs5E51w08rSGE\nDQ1LLLU19NjeaCir7EFzuE79f7PZqKrCVbC/uV7gCvE31fi5iRBPvdqPTQSYjRiaEJzO5O+rtNxB\nblEZOVZbrdtcq40caxm5RbZ6wzdNBkV0qMUd9MI9PX01evtiwwKIDPaToXE+qqH3l8Vs4LZR3Qk0\nmxodOlm56qQMnRQdXVtuW3Al8BLubQsWaq2fU0rNBjZprVPq1F2LBDrhJeXOctYcWsNnaZ+xPmc9\nLu1iYORAknslM6n7JMIt4Wd2wGMH3b1vDQ2jrAxwCSPd89KEEABopxN7Zmb1MMmawa2iuqfHHB+P\nf69e7iGSiYnueW49e7T6Hm+j564mu6isXnmoxcRto7pXBbDjzRyWGGA2VgeuygBWM4TVCF4NhbIg\nT5m/yeD1D6sttcql1priMoc73FnLyCmykesJeu4yG7lWW1XPSyU/o8EztLN6OGd1+HP3/oUFmL3+\n9yQaZ3e6e6OLy+wU2+wUlzkottn50+KdFJU1vOhX5dDJwQnhDPbs+dYnOgSTDJ0UPkQ2FhcC9weE\nbQXb+Cz1M1ZmrKTEXkKXwC5M6TmF5F7J9AzvefoHqzmMMm01HE1zl4fGQa9L3QGux1gIimiVaxGi\nI9FOJ/asrPqLk6Sn1w5uXbvi1zvR0+vW233bq2ebbc7tcmkOFB5nR1YR2zOtvL0+o9G6DQ1LDLaY\nCfY3egKZuX5Iq/M4xN9MkL9RPnQ2kdaawuMV5Flt5NTp3asMgfnFtnq9mwFmoyfoNT6nL0SG4TWZ\n3enyhLHaoazEVjugNVSn2GbnRIXz1CepY+esCfKaCZ/XJvvQCdFeZZdmk5KWwtK0pWSWZBJgCuCy\nbpeR3CuZkTEjMZ7OSo5VwyhXu4dS1hpGOQZG/vbkwyiFOAtol6vx4FZeXlXP1DUW/8REgkaNqh4y\n2asXhqC225Rba012URk7sqxszypiZ5aVnVlWSjy9agFmI35GAxXO+nPOYsMsfP/YuCYNSxQtRylF\nZLA/kcH+DIgLa7CO06U5UlpeHfiKysjz9O7lWMtYt/8Ih0ts1B3RGuJvIjbcQkxYde9e5Vy+ytsA\nv3a+CnATVThcnvBVP2w1/Lh2vbpDZesyGhShFve8zFCLmdAAE71Cgqvuu2/NhFiq74cGmLht4Qby\ni8vrHS8uPEDCnBB1SKATPuG4/TgrM1aSkpbCpnx3D++ImBHcnXQ3488ZT5D5ND44nmwY5agHZRil\nOGtplwt7dnb1EMk0T4BLT0fbbFX1TLGe4HbBBdXz3HolYgxuu+BWqaCknB1ZRezIslbdFnrm2piN\nir4xoSQP7sqg+HCSEsJIjApm2Y7cBueEPTapr4S5DsLomXcXHWphSCN17E4Xh0vK68/lKyojr9jG\nnpxijpTWDxLhgeYaPXy1F3LpGm4hJsyCv+nkoa81NmKvcLhO2gN2qlBms5984RyjQREWYK4KZSEW\nE10aCGS17tf4WaCfsUlDXp+44rwGfx8fndjnjI8lhK+TQCc6LKfLyU95P5GSlsKqg6uwOW10C+nG\njMEzmNJrCl2Du578ACcbRnneVTKMUvgM++HDZP/+D8S/+PeT7qemXS7sOTnVq0lW9rqlp6PLqueW\nmWJi8E9MpNOIEfh7hkz6JSZiDA5ui8upx1pmZ1e2u+dtR6Y7wOVY3UHToCCxSzCX9u3CoPgwkuLD\n6Rsb0uAH78oP1i39gVu0L2ajgbjwAOLCG19Mp9zhJN9a3sicPhubDx2j6ET9+V2RwX5VQa/unL4d\nWUX87cufqwJU5UbsDqeLS/p0OWUoK7E1LZCZDMoTsqp7yWLCLDXCl6leCKv5OMDctEDWXPL7KMTp\nkzl0osNJL0rns7TPWJa+jMMnDhNiDmFSj0kk90pmUNSgxv/jqTWMcjVkbaw9jLJqNcreMoxS+JTc\nWX+m6H//I/zmm4l95mm0y4UjN7fe4iTl6enoEyeqnmeKjq6zOIn7jzEkxGvXUlbhZHeOle01et4O\nePaYAjgnIpCk+PCq8Na/ayhB/vLdpWh5ZRVOd8irMaevbvgrOYP9+xpjquwhO2n4ql8e4uVAJoRo\nHlkURficIlsRKw6sYGnaUnYV7sKojIyOG82UXlO4NOFS/I2NDIM8drA6wB34BmxWQEHXwe7w1vNS\nGUYpfJbWGtuuXWRMvcW9v5rBgP+552I/dAhXzeAWFVUntPXGP7EXxtBQL7bePZTs57wSd8+bJ7zt\nP1yK0zMBKibUQlJ8GIMSwhkYF0ZSfBjhgX5ebbMQNZXY7FUrdN62cEOj9Z69un+jvWQWs/dXMxVC\ntD1ZFEX4BLvTzrfZ37I0bSnfZH2Dw+Xg3E7n8sjwR5jcczKRAZH1n2QrhozvqhczqTWMcooMoxQ+\nSWuNIy+vurctzTPXLTUN1/Hq3itcLhzHjhF2/fXu4NY7Ef9evTCGNbzARFtyujRpBaVszyxiZ7a7\nB25vbvUGwZ0CzSTFhzOhXzQDPT1wXULPbONwIdpaiKen7NzoEOLCAxrcFiMuPIBbL+ze9o0TQvgM\nCXSiXdFas6dwDylpKXx+4HOOlR+js6Uzv+77a5J7JdO3c9/aT3A63MMoKxczydwA2gnmIPem3iPv\nlmGUwmdorXHk59cObftTKU9Lw1VaWlXPGBmJf2IiIRMnYk1JAUf1sC9XURGRd//2pHPpWpvWmkNH\nT1QtWLI9y8rubCvHPcuXB/kZGRAXxu2jurt74OLDie8UIL0UokN7dGIfWeRDCNEqJNCJduHwicMs\nS19GSmoKadY0zAYzlyZcSnKvZEbFjcJsqLFE8bEMd+9bQ8MoL3rIHeDiR4JJhl6JjklrjePw4erF\nSap63tJwlZRU1TNGROCfmEhYcnKtxUlMnToB7rlzdb/I0C4XBa++RuwzT7fZ9eQX29ieWVS9ZUC2\ntWpBCT+TgX6xodwwLN499y0hjJ6RwbKqpPA5ssiHEKK1SKATXlPmKGP1odUsTVvKD7k/4NIukqKS\neOqCp5jYfSJh/p5hYLZiyFhZYzXKdHd5aBycl+ze2FuGUYoOSGuNo6CgdmhL9QS34uKqesZOndzB\nbcpV1fPceveuCm6NKdu2zT13ria7nbKtW1vjcgA4dryCHdlWdmS6e952ZhdV7SVlNCjOjQ5hUv8Y\nkuLDSYoPo09MCGbZaFucJa4ZEicBTgjR4iTQiTaltWZz/maWpi/ly4wvOW4/TmxQLHcOuJPkXsl0\nD+tePYyy5mqUtYZR3iPDKEWHorXGeeRIvQ24y9PScFmtVfWM4eH4JyYSOvnKWouTmCKa9mVFzyWf\nttQlNKi03MGubGuN/d6sHDpavdhKz6ggRvWKJMmz4mS/2FCf3ZxZCCGE8BYJdKLFLU9fzvwt88k7\nnkdMUAwzh84kKSqJpWlLSUlLIbs0mwBTAOPPGc/Vva5meMxwDEWHYL9nIRMZRik6KK01zsLCOr1t\n7nluzprBLSwMv96JhF4xCf9eiVXDJY0REe12npjN7mRvbrF7wRLPXm+pBaVULpQcFx7AoIQwpp7f\njaS4MAbEhxFqMZ/8oEIIIYRoNtm2QLSo5enLmbV+FjanrapModBoFIqRsSO5utfVXNZlBIHZmxsY\nRhnvHkLZaxz0HAuBnb1yHUKciqOwsF5oK09NxVlUVFXHEBpatXdb1aqSiYkYIyPbbXADcDhd7D9c\nWrVgyY6sIn7OK8HudP9/ERnsz6D4MAZ6FiwZGB9GZLBs/SGEEEK0FNm2QHjN/C3za4U5AI0mxBzC\nJyOeJjZ7O6x9pfYwyh5j4PzfufeEk2GUop1xHD1atapkRY2eN+exY1V1DCEh7lUlx4+vvThJVJTX\ng9uSrdknXYTB5dJkFB6vWrBkR5aV3TlWbHb3dgEhFhNJ8WHcNaZn1WbdsWEWr1+XEEIIIdykh060\nqKR3BtLQO0ppzY6MTNzDKIdU98LJMErRTjiOHau/OElqKs6jR6vqGIKDa/W0VS5QYurSpV0GnCVb\ns+stk+5vMjB1ZAL+ZhM7s90BrsTm3tbAYjYwoGtY1WqTSfHhnNM5UFacFEIIIdqY9NAJr/gp9yfQ\nusEethinC254S4ZRCq9zFhVVL0pSM7gVFlbVMQQF4Z+YSPC4S91z3DwhzhQd3e6Cm9aacoeL4jI7\nxTY71jIHxTY7xWV2nknZXSvMAZQ7XLy1/iBmo6JvTCjJg7oyKD6cpIQwEqOCMcmKk0IIIUSHIoFO\ntIhP93/K7B9mE+V0YjUYKDdUfyi0uFzMPFoEA67zYguFL7EfPkz27/9A/It/b3SDbKfV2sCqkqk4\nC45U1TEEBuKXmEjwJZfU6nkzxcS0WXDTWmOzu6pCWLHNTrHN4bnvqC6rEdSKbQ5KapRXOF1ndE4F\n7PrzRPxNsuKkEEII0dFJoBPN4tIu5m+Zz8JdC7kwpCf/t/Mbvg0MYH6ncPJMRmIcTmYeK2KySfaI\nEy3nyKuvUbZ5MwWvvkaXhx+qFdwq0tz3HQUFVfVVYCD+vXoRfNGY2sEtNrbZwa3BQFYnfJ2qvHKx\nkcb4mwyEBpgJtZgIDTATHmAmoVOAp8xMaIDJc1tdJ9RiZtobP5FXbKt3vK7hARLmhBBCCB8hc+hE\nk5U5ynhy3ZN8dfArbgw9jye2f4k5sg8UHQJHWXVFcwBMeRmSbvJeY0WHpp1O7Dk5VGQcZMvXP9D5\nw7cxahcad29TJRUQgH+vXlWhza9XL/wTe2PuGosyNDyUUGtNmd1ZJ2ydWShzuM4skDUUvhoLZSEW\nExZz08JXQ3PoAsxG5lw3UDY3FkIIIdoxmUMnWt2RsiM8uPpBdh3ZxSMh/fnN9s9R/a6B616HPZ/B\nqtlgzYKweLjsaQlz4pS004k9Nw/7oYNUHDxIRYbn9uBBKrKywG4HIAqqFt5xodjfuRv+t00n6eKh\nlIZFcaTCWTt8/XyC4u37qoOYp7zEdvqBzGI21ApbnYP86B4RVCeE1Q9lIc0MZM1VGdpOtsqlEEII\nITo26aETZ2z/sf3cv+p+imzHmGNK4LJ9q9zbDkycA430gggBoF0uHPn59QPbwYPYMzPRFRVVdZXF\nAnHxVER3pSQiloLwLnyWVsJDP76Hn8tRVa/cYOaOCU9wzBLa6HkDzMYGw9apAllogDuQyfBEIYQQ\nQrQl6aETrWZd9joe+eYRAo0W3nJ2on/qKrj8zzB6puwfJwD3EEbH4cOewJZRHdgOHqTiUCa6vLy6\nrtmP8i6xlETEUHB+P7KCIkn168ReYxhprgC0qvEFQQncn/OJeyXVGpR28et9X5Mw+5lGe8n8TPJF\ngxBCCCF8U7MCnVJqEjAfMAJvaK3n1vn574D7ASdQCtyttd7TnHMK71m0bxFzNsyhd2h3XskrIObw\nTrj2XzDoZm83TbQxrTWOggJ3SDt4kIqDh2r1tmlb9UIcLpOJ4k5dKAjtQlafMaT6dSLdvzPZQZEc\nCQirCm2dAs3EhAUQG2ZhZJiFq0MtxIRZiA0LICbMff/7sS/hp2svw++nnQyyHmLiyG5t+ncghBBC\nCNEeNDnQKaWMwAJgPJAFbFRKpdQJbP/VWv/TUz8Z+DswqRntFV7gdDmZt2ke7+19j0u6DOdv+zYR\neKIQpn4IiZd5u3milWitcRYWUnHoUFVvW2naAWwZB9FZmRhs1QvfOA1GjoREkBkYSWbcCLKDIskN\njnSHtqBOdA5xB7WYUAuxYRYmeoJbjKcsJsxyWvPMyl59m+saWeRDCCGEEOJs1JweupFAqtY6HUAp\ntQi4GqgKdFrr4hr1g6hey0B0ECfsJ3js28dYm7WWafGX88imJRgNRrhjOXQd4u3miWbSWuMsKsJ2\nIINjP6diTU3HduAgZGfil5+D2Xaiqq5TGcgL7ExOcCQ5XYeRExRJbkgUFdFdMcd1JbpTsKdHzcJA\nT3CLCbPQJcTSYkMeZZEPIYQQQojamhPo4oDMGo+zgPPrVlJK3Q/8HvADxjV0IKXU3cDdAN26ybCp\n9iLveB4PrH6AX479wp+6X8Ovv/s3hMTArYuhc09vN0+cJrvTRX7WYQr27ce6P53yDHdg88/LIfRo\nHgHlJ2rUVhQHdiInOJK8rkMojYrFHhuPMSGBwG4JRHcOJjbMQpKnhy0y2B+joW3nTl4zJE4CnBBC\nCCGER6sviqK1XgAsUEpNBf4fcFsDdV4HXgf3Kpet3SZxansL9zJj1QxK7aW80u0axqx9GWIHwdSP\nIDjK283zKUu2Zje5x8lmd5JfbCPXauNwzhGKU9OpOHgQsg5hyc8h5Gg+XayHCbWfwAJYcC/1fyQw\nnMJO0WT2Pb8qsAX07EHnnt2JiQxhUJiFzkF+zd50WwghhBBCtK7mBLpsIKHG43hPWWMWAa8143yi\njaw5tIbHvnuMML8w/hN1KX3WvgSJl8ON74B/sLeb51MqN362lBxj7sb3mDtiGk8sdi/dP75fNHnF\nNvKs7sCWZy3jSP5RT2DLxHI4l07H8uh6/AhxpUfoXXG81rGtIZ05HtWVoj59KO3WjcAe3el0bi/i\n+vaiX1iQhDUhhBBCCB/QnEC3EeitlOqBO8jdDEytWUEp1Vtrvd/zcDKwH9Fuaa15d8+7zNs0j/4R\n/XjZ2Ymo9a/BoKmQ/DIYzd5uos8oLXeQW1TGs8v2UGZ3Mn3fVwwoPMCv933NwgGT+ce/lvOxJ6h1\nPV5AXOkRhh4/Qqfy0lrHsYVH4IyNxzh8IMaePeh0bi9CEnvg160bBovFS1cnhBBCCCHaSpMDndba\noZSaAXyJe9uChVrr3Uqp2cAmrXUKMEMpdTlgB47RwHBL0T44XA7m/DSHD3/5kPEJ43guL5+AX/4L\nY/4A456SPebOgM3uJNdqI7eojJyat9Yy8o6doPTwEfyLCokss3K+zUpcSQFXZPyIAc1VGeuZkrG+\n1vFcnSIwdetGYM8hBPTogd855+DX/Rz8EhIwBAZ66SqFEEIIIUR70Kw5dFrrFcCKOmVP17g/sznH\nF22jpKKER755hPU565neZyozd6/GkLkRrngBzr/b281rVyocLvKLbeQUlZFrtZFjLSO3yB3WCgqs\nVOTnYzp6hEhbMRE2KxFlViJsxVxQUUyUrZjQE1aMrtr7qNWcNOpCsT88gTWDx/O3mZPdPW1BQW17\nkUIIIYQQosNo9UVRRPuWXZrNjFUzyLBmMGvwg1z/3etw7CDc+Db0v8bbzWtTDqeLwyXl5FrLyPGE\ntJwiG3lFJyjJPUxFfj6Go0fcIa2smEiblQiblcTyYiLKigmsOFH/oAGBmKOjMSdEY4oeiDk6GlOX\naEzRXTBHR7M29SixTz2Iv8sBgBFNj5JcTL+aiOW889r4b0AIIYQQQnQ0EujOYjsKdvDA6gewO+28\nNuwxLvhiFlScgFs/he6jvd28FuVyaY6UlpPjWVykMrAdPmLlRHYu9vx8jIVH6FTm7lWLtFmJLCvm\nvHIrncqKMWpXreNpgwHVqTN+MdH4xfTHHN3FE9Si3fej3feNwSdfRGbwp3/mqAGocXiz0gz/9lO4\nVDbLFkIIIYQQJyeB7iy1MmMlf1r3JyIDInlrwP30TPkD+AXD9C8gup+3m3dGtNYcO2GvGgaZay0j\n5+gJinLyKcvJw3HYHdbCTxQR4QlqXW1WkmxWguy2esdzWQIwRHXBv3c0lthBVUGtslfNFB2NKSIC\nZWr+r0/Ztm0YHI5aZQaHg7KtW5t9bCGEEEII4fsk0J1ltNa8uetN5m+Zz+CowczvOpHOi+93bxQ+\n7RMIi/d2E2vRWlNsc5DrmauWYy3jcH4RxVk52HLycBbkYyg8QviJ6vlq59isDLEVY6rbq6YMuDp1\nxhDVBUtsPwK6xnoCmieodelyWr1qLannkk/b7FxCCCGEEML3SKA7i9iddmb/OJslqUu4oscVPGvq\nhv+SGdDtArj5vxDYuUXOs2RrNq9/uoHbV73BW5fdxT3Xjmx0o+zj5Y7qOWvHSinMzKckO4fy3Hxc\nRwowFh4h9PgxIsvci4ycW2ZlqKN+r5rDEoCOiMLYvQsBXQcSVDestWCvmhBCCCGEEO2FfLo9S1jL\nrTy89mE25m3kd0n3cF9BPmr9k9D3Krj+DTAHtMh5KjfKnr5xKf0LD3DphmX80Wlh88/ZdNcnKM3O\npSIvD11QgOnoEUJKi6oWF+lvK6k3V82lDNjDOqEjozCd25fA2GhC4rviFxNdHdS6RGMMlpUgbUYo\n8QAAIABJREFUhRBCCCHE2UcC3VngUPEh7l91P9ml2Tw/6lmm7FwBO/4HI+6CK/4GBmOLnetvX+7j\nnLw0rjjo3ldtcsZ6LsvcTOAn5fXqVvgHYO8UAXFR+EX3wxIXS1hCVyyxMVVBzRQZgTK2XPuEEEII\nIYTwJRLofNyW/C3MXOPeDvDfY+czbO3fIW01jPt/MOaRFtswXGvNqi0ZTPr+Y67bvxZDjZ/lB3Zi\nbcJQHp12EYFdY91BrUsX6VUTQgghhBCimSTQ+bBl6ct4+vuniQuOY8GFs+n22UOQtxOuXgBDprXY\neXZlHmPZvDcZs/ZDbiwvwYlCebbLNgBdjxeyc8BFRF13bYudUwghhBBCCCGBzidprXlt+2u8tv01\nRsSM4MWkBwn7321Qehh+/QGcO7FFzpNntfHOm8vo89HrXH0sk9KefbBGRBK4+UeMLmdVPQMunjr2\nIyCBTgghhBBCiJYkgc7HlDvLefr7p1lxYAVX97qaZ86Zgvnd6wENty2D+GHNPseJCgdvp2xE/ftV\nkg9uoiy0E+HPPkff66/hwHXXU14jzAGYXU5iMn9p9nmFEEIIIYQQtUmg8yFHbUd5aM1DbD28lZlD\nZ3KnfzfUf66BoCiYthgiE5t1fKdLs/jHdPa+/C+u2vklfrgw33oHfR66H0OQez6c7KsmhBBCCCFE\n25FA5yPSrenc//X9FJQVMO+SeUwsKoQPbobo/nDLxxAS3azjr99fwJIFHzDp2w+58UQhjlEXc+6s\nJ/Hr1q2FrkAIIYQQQghxpiTQ+YCfcn/i4bUPYzaYeXPCGwz6eRWsmg09x8JN74IltMnHTiso5fV3\nviLps7e4o2A/5fHnkPCPvxI8enSLtV8IIYQQQgjRNBLoOrhP93/K7B9mc07oOSwY9zJx370MG/8N\nA2+Eq18Fk1+Tjnv0eAWvpWzG/O6b3HrgB1wBAXR+/Am6TJuKMsnbRgghhBBCiPZAPpl3UC7tYv6W\n+SzctZBRXUcxb9RzhCx7GPamwKgH4PLZYDCc+kB1lDucvPNdGvveeJebdq4gxF5G4PU3kPCHhzF1\n6tQKVyKEEEIIIYRoKgl0HVCZo4wn1z3JVwe/4qZzb+KJpN9h+vA3cPB7mDgHLrzvjI+ptWbFzjwW\nv/UZ133/IWOKc2HwMHr8+Sksffq0wlUIIYQQQgghmksCXQdzpOwID6x6gN2Fu3l0+KPcGjcO9fYU\nKEyF69+EgTec8TG3HjrGKx98x/lfvs8jOTtwdokh7tn5hEwYj1KqFa5CCCGEEEII0RIk0HUgvxz7\nhRmrZlBUXsRLl77EOEssLJwAtmKY9gn0vOSMjpd17AR/T9lO0OL/8mDqN5iMBiIeeIDIO6djsFha\n6SqEEEIIIYQQLUUCXQexLnsdj3zzCEGmIN6e9Db9Sq2wcCKYAmD65xAz8LSPVWKz8+qaVPZ/8Am3\n7VxGZJmVwCuuoOsfH8UcG9uKVyGEEEIIIYRoSc0KdEqpScB8wAi8obWeW+fnvwfuAhxAATBda32w\nOec8Gy3at4g5G+bQO7w3r1z2CjEHN8And0F4N3fPXKdzTus4DqeLRRsz+XTR1/x6w8ckH83A0Kcv\nCU8vIHDYsFa+CiGEEEIIIURLa3KgU0oZgQXAeCAL2KiUStFa76lRbSswXGt9Qil1L/A34FfNafDZ\nxOlyMm/TPN7b+x6XxF/C3y7+G4Fb34cVj0L8cJj6IQR2Pq1jrf35MPM//omLv/2Y2Qc3osLDif3L\ns4Rdey3KaGzlKxFCCCGEEEK0hub00I0EUrXW6QBKqUXA1UBVoNNar6lR/0dgWjPOd1Y5YT/BY98+\nxtqstUw7bxqPDPsDxrVz4Lt5cO4VcMNC8As85XH25RUzJ2UnEV8u4alfvsbishNxx21E3ncfxpCQ\nNrgSIYQQQgghRGtpTqCLAzJrPM4Czj9J/TuBzxv6gVLqbuBugG7dujWjSb4h73geD6x+gF+O/cKT\n5z/Jzb2vh2UzYet7MPQ3MPlFMJ78pTtcYuPFr34hdelK7tm1lLiSwwSOGUPME0/g37NHG12JEEII\nIYQQojW1yaIoSqlpwHCgwWUYtdavA68DDB8+XLdFm9qrPYV7eGDVAxx3HGfBZQu4KGoILJoK+1fC\nJY/D2MfhJFsJ2OxO3vgunSVLf+A3W5bwm/y9GM85h67z/knwJWe2CqYQQgghhBCifWtOoMsGEmo8\njveU1aKUuhx4ErhEa13ejPP5vNWHVvP4d48T7h/Of674D+f6dYZ3pkDOVrjqJRh+R6PPdbk0n23P\n5pWUbVy6cRkvpa/DaLHQ5Y9/pPO0W1B+fm14JUIIIYQQQoi20JxAtxHorZTqgTvI3QxMrVlBKTUE\n+BcwSWt9uBnn8mlaa97d8y7zNs2jf0R//nHZP4gsK4E3J0BxNvzqfeh7ZaPP33DgKM8t20X0918z\n5+cvCLaVEn79dXR56CFMkZFteCVCCCGEEEKIttTkQKe1diilZgBf4t62YKHWerdSajawSWudArwA\nBAMfKfcwwUNa6+QWaLfPcLgczPlpDh/+8iHjzxnPcxc9R8Dhn+H9G8Flh9+kQLeGpyZmHDnO3M/3\ncejbH5ixJ4UehZlYhgwh5sknCRjQv42vRAghhBBCCNHWmjWHTmu9AlhRp+zpGvcvb87xfV1JRQmP\nfPMI63PWc+eAO3lw6IMY0tfC/26FgE4wbRlE9an3POsJOy+v3s/yVdu4Y/dyZh7agrFLNNEvvEDo\nVZNRJ5ljJ4QQQgghhPAdbbIoiqgvuzSbGatmkGHN4M+j/sx1va+DHR/Cknshqi/c8jGExtZ6ToXD\nxXs/HuS1lbu5fOcqXk9dg1lpIu79HZG//S2GwFNvYyCEEEIIIYTwHRLovGBHwQ4eWP0Adpedf47/\nJ+fHjITvX4avnoLuY+Dm98ESVlVfa83KPfnMXbGXrjt+5OWfVxBefISQCRPo8sdH8YuP9+LVCCGE\nEEIIIbxFAl0b+zLjS55c9yRRAVEsuHwBPUO6w5d/gh9fhf7XwrX/ApN/Vf1d2VaeXbaH/G27ePjn\n5Zyb8zP+555L9MsvEHTBBd67ECGEEEIIIYTXSaBrI1pr3tz1JvO3zGdw1GDmj5tPZ1MQfHIn7F4M\n598LE58HgwGAXGsZL3z5M1//+At37f+Ky1PXYwwNocszTxN+440ok7x0QgghhBBCnO0kFbQBu9PO\n7B9nsyR1CVf2uJLZo2fjb7fBe9dDxncwfjaMehCU4ni5g399k8Yb3+xnQup63t3/FX7lZXSa+mui\nHpiBMTzc25cjhBBCCCGEaCck0LUya7mVh9c+zMa8jdw76F7uHXQvqiQP3r8BCvbBta/DoF/hdGk+\n3nSIeSt/IS59F6//spyIgiwCL7yA6CeewHLuud6+FCGEEEIIIUQ7I4GuFR0qPsT9q+4nuzSbOWPm\ncFXPq6DgF3fPXNlRmPohJF7Guv1H+MvyPRSlZfBY+hf0T9uKOT6e6Ff+QfBll8k2BEIIIYQQQogG\nSaBrJZvzN/PQmocA+PeEfzMsehhkboD/3gQGE9y+nFRTL55/eyPrd2Vy16FvuWLvaoxmE5EPPUTn\nO27H4O9/irMIIYQQQgghzmYS6FrB0rSlPLP+GeKC41hw2QK6hXaDnz+Hj+6A0FiOXbeIv2+w88FP\n3zAhZxuL9n2Ov/UoYVcnE/X732OOjvb2JQghhBBCCCE6AAl0LUhrzavbX+Wf2//JiJgRvDj2RcL8\nw2DzO7DsIVwxg/lPz7/xf//OIC7/AG+lryDy0H4sAwcS/adXCBwyxNuXIIQQQgghhOhAJNC1kHJn\nOU99/xSfH/icaxKv4ekLnsZsMMHaubB2DvnRY5h27F6OLE/lqew1DNz5HcbISLo8/zxh11yN8mxX\nIIQQQgghhBCnSwJdCzhqO8pDax5i6+GtzBw6kzsH3IlyOWHpTNjyDqstl3Nf+lTuKFjPVdu/wOCw\nE3HXnUT87ncYg4O93Xzhg+x2O1lZWdhsNm83RQghxFnGYrEQHx+P2Wz2dlOEOCtIoGumdGs69399\nPwVlBcy7ZB4Tu0+EihOUfXA7AQe+5B/2q9mRN4RFe1/FPz+H4LFjiX78Mfy6d/d204UPy8rKIiQk\nhO7du8sqqUIIIdqM1prCwkKysrLo0aOHt5sjxFlBAl0z/JT7Ew+vfRizwczCiQtJikqi5Fg+1jev\np2vJLl603siwgye4fO/r+PXsSfS/Xyd4zBhvN1ucBWw2m4Q5IYQQbU4pRUREBAUFBd5uihBnDQl0\nTbR4/2Ke/eFZuod155XLXiE6IJbFq9cz5Nu7iC4vYO2hCUza9hOGwACinnicTlOnomTogWhDEuaE\nEEJ4g/z/I0TbkkB3hlzaxfwt81m4ayGjuo7ihYtfYPMBG39e+h9mFz8DGYqD+84htnQX4TfeSNRD\nMzF17uztZgshhBBCCCF8kAS6U1ievpz5W+aTdzyP6MBoIgMi2VW4i5vOvYlru93Pfe/uxZn+Da8U\nvUzRtmAqjioChvcl5k9/wtKvn7ebL4QQQogOICMjg6uuuopdu3a1+LHXrl3LvHnzWLZsGSkpKezZ\ns4fHH3+8Scfq3r07ISEhGI1GTCYTmzZtauHWCiHOlKyVfxLL05cza/0sco/notHknchjV+EuLo+f\nRElWMlNe+YHeP3/EC5te4/DqEFzmLsT9/f845913JcyJDmXJ1mxGz11Nj8eXM3ruapZszW71c155\n5ZUUFRVRVFTEq6++WlW+du1arrrqqlY//5maNWsWcXFxDB48mMGDB7NixQrvNGTHh/DiAJgV7r7d\n8WGrn7KjvVYfffQR/fv3x2Aw1PuwOWfOHBITE+nTpw9ffvmlV9q3PH05Ez6eQNI7SUz4eALL05e3\n+jk72mt4st+39vAaAtgPHyZj2q04OthcseTk5CaHuUpr1qxh27ZtEuaEaCck0J3E/C3zsTnrL/v+\n1YEfWb7pAP/Nf4tfrVhKaU4AkfdMp9cXXxJ65ZUydlx0KEu2ZvPE4p1kF5WhgeyiMp5YvLPVQ92K\nFSsIDw+v9wGzLTkcjjOq//DDD7Nt2za2bdvGlVde2UqtOokdH8LSB8GaCWj37dIHWz3UdbTXasCA\nASxevJiLL764VvmePXtYtGgRu3fv5osvvuC+++7D6XS2dFNPqu4XhbnHc5m1flarh7qO9hpCw79v\n7eE1rHTk1dco27yZgldfa7FjOhwObrnlFs477zxuuOEGTpw4wezZsxkxYgQDBgzg7rvvRmsNwMsv\nv0y/fv1ISkri5ptvBuD48eNMnz6dkSNHMmTIED777LN653j77beZMWMGALfffjsPPvggo0aNomfP\nnnz88cdV9V544QVGjBhBUlISzzzzTItdoxCi5cmQy5PIPZ4HQHip5qElTl68xog1CEbtP8oj38zG\nWVRGcP8Iov/+HuZzenq5tUI07M9Ld7Mnp7jRn289VESF01WrrMzu5I8f7+CDDYcafE6/rqE8M6X/\nSc/7wgsv4O/vz4MPPsjDDz/M9u3bWb16NatXr+bNN9/k+++/Z9OmTTz++OOkpaUxePBgxo8fz+TJ\nkyktLeWGG25g165dDBs2jPfee6/RL0q6d+/ObbfdxtKlS7Hb7Xz00Uf07duXo0ePMn36dNLT0wkM\nDOT1118nKSmJWbNmkZaWRnp6Ot26dWPixIksWbKE48ePs3//fh555BEqKip499138ff3Z8WKFXRu\nq3mwnz8OeTsb/3nWRnCW1y6zl8FnM2DzOw0/J2YgXDH3pKf1tdfqvPPOa/D8n332GTfffDP+/v70\n6NGDxMRENmzYwIUXXnjSv58z8dcNf2Xf0X2N/nxHwQ4qXBW1ymxOG09//zQf//Jxg8/p27kvj418\n7KTn9bXXsDFt8RrmPf885Xsbfw0BdEUFZTt2gNYULVpE+d69J134zP8891SMU/n555958803GT16\nNNOnT+fVV19lxowZPP300wDceuutLFu2jClTpjB37lwOHDiAv78/RUVFADz33HOMGzeOhQsXUlRU\nxMiRI7n88stPes7c3FzWrVvHvn37SE5O5oYbbmDlypXs37+fDRs2oLUmOTmZb7/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IAAAg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\nneE0RbsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faf29c79e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ln_solvers_bsize, solver_bsize, batch_sizes = run_batchsize_experiments('layernorm')\n",
    "\n",
    "plt.subplot(2, 1, 1)\n",
    "plot_training_history('Training accuracy (Layer Normalization)','Epoch', solver_bsize, ln_solvers_bsize, \\\n",
    "                      lambda x: x.train_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n",
    "plt.subplot(2, 1, 2)\n",
    "plot_training_history('Validation accuracy (Layer Normalization)','Epoch', solver_bsize, ln_solvers_bsize, \\\n",
    "                      lambda x: x.val_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n",
    "\n",
    "plt.gcf().set_size_inches(15, 10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inline Question 4:\n",
    "When is layer normalization likely to not work well, and why?\n",
    "\n",
    "1. Using it in a very deep network\n",
    "2. Having a very small dimension of features\n",
    "3. Having a high regularization term\n",
    "\n",
    "\n",
    "## Answer:\n",
    "\n",
    "？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
